Amplify

The agent library

Agents doing real work in real operations

Thirty-seven agentic solutions built with enterprise clients across mining, building materials, manufacturing, logistics, healthcare, finance and investment. Each one started as a specific business problem, connects to the systems and documents the work actually depends on, and is held to a value case agreed with the client.

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Agents in this library
37
Already in production
14
Recurring patterns
6
Sectors
7

How to read this library

Every agent described here has been built for a named enterprise client. Many are in daily use; others are in user acceptance testing, prototype, feasibility or design. Each card states where it stood at the last review, because that changes how much weight a number deserves.

The value figures are business cases, not audited results. They are the targets we developed with each client during scoping and the benchmarks the work is held to. Formal post-deployment measurement is underway with several clients and we will publish it once it exists. Where a figure would claim more than we can currently evidence, we describe the intended outcome instead of inventing a percentage.

Clients are described rather than named, and commercial detail is deliberately omitted. We are happy to walk through any of these in a call, including the parts that were harder than expected.

Six kinds of work

Organised by what the agent takes on, not by industry

The same handful of patterns keeps recurring across very different businesses. A process engineer diagnosing a kiln stoppage and a finance lead explaining a cash movement need the same shape of agent. Grouping the library this way makes it easier to find the pattern that matches a workflow you already recognise — then filter by sector if you want to see it in your own context.

Diagnose Find the cause, surface the anomaly, explain the movement. 6 agents Optimise Choose the best option inside real physical and commercial constraints. 5 agents Extract Turn documents, drawings, images and records into structured data. 7 agents Assure Check work against a standard before it progresses to a decision. 6 agents Advise Put institutional expertise next to the person doing the task. 9 agents Forecast Produce the recurring numbers, scenarios and the pack around them. 4 agents
Sector

Pattern one

Diagnose

Something has gone wrong, or moved unexpectedly, and the explanation is buried in data nobody has time to interrogate. These agents do the interrogation — systematically, the same way every time — and hand a person a hypothesis they can act on or argue with.

Root cause analysis for kiln stoppages

Dual-mode analysis for a cement business: rapid hypotheses during a live disruption, rigorous investigation afterwards.

In user acceptance testing Building materials Read more Close

The problem

Root cause analysis for kiln stops and production disruptions ran from days into weeks, leaned heavily on the experience of a few individuals, and lacked mathematical rigour or a standard method. Corrective action was delayed at exactly the moments it mattered most, and the quality of the analysis varied with who was available.

How it works

The agent is connected to the plant's technical data management system and its site-specific standard operating procedures. Firefighting Mode produces candidate root causes within minutes for a live disruption. Deep Analysis Mode runs a fuller investigation, taking operator input and experiential knowledge as evidence alongside the process data.

The value case

Bring an RCA cycle down from days to minutes, and let a less experienced engineer run an investigation to the standard a specialist would. The commercial case built with the client centres on avoided kiln stoppages and engineering capacity released back to fixing problems rather than diagnosing them.

Blast fragmentation analysis

Automated image analysis, quality assessment and reporting for an international gold and copper producer.

In production Mining Read more Close

The problem

Fragmentation analysis was a manual effort in specialist desktop software — image capture, interpretation, then report compilation. The workflow throttled blast optimisation cycles and produced results that differed between operators and between sites, which made comparison across a portfolio unreliable.

How it works

A fragmentation analysis agent accepts blast images, performs the analysis, calculates the key size metrics, evaluates blast quality against expectation, generates operational recommendations, and compiles the report. The engineer reviews and signs off rather than assembling from scratch.

The value case

Turn an hours-long workflow into minutes, and hold analysis consistent across operators and sites so that blast optimisation decisions rest on comparable numbers. The scoped target was roughly an 85% reduction in analysis and reporting time — a target, measured against the manual baseline, not an audited outcome.

Financial transaction anomaly detection

Detecting anomalies in transaction and pricing data across invoices, rebates and freight for a cement producer.

In user acceptance testing Building materials Read more Close

The problem

Commercial pricing review was manual end to end: extract the transaction data, hunt for deviations, compile something for leadership. The consequence was blind spots and slow correction — price deviations, over-applied rebates and inflated freight costs went unnoticed between review cycles.

How it works

The agent runs a set of detection modules across invoice prices, rebates and freight, quantifies the exposure attached to each anomaly it raises, and produces an executive-ready summary. An optional deeper pass looks for root causes and surfaces the specific records an auditor would want to see.

The value case

Compress a days-long review into a repeatable run, and make leakage visible across markets and customer segments rather than only where someone happened to look. The value case agreed with the client covers recovered revenue and the analyst time returned; measurement against it is in progress.

Multi-agent maintenance diagnostics

Six specialised agents handing off through a full analytical workflow, from raw data to a client-ready deck.

Proof of concept Professional services Read more Close

The problem

An operational performance consultancy wanted to know whether an agentic platform could carry a genuine end-to-end analytical workflow — ingestion, cleaning, diagnosis, validation, presentation — rather than a demo that falls over when the data is real. The test case was maintenance diagnostics and optimisation.

How it works

Six specialised agents hand work between them: data ingestion and validation, trend and scenario analysis, financial impact sizing, benchmarking against industry standards, mapping findings into the firm's own improvement framework, and compiling the final client-ready output with visualisations and narrative. Retrieval guides how the analysis is planned, not just what gets quoted back, and independent sub-agents run the quality checks on a clean context so they are not biased by the work they are checking.

The value case

Validate that complex, multi-step analytical work can be delegated to agents with a human in the loop at each checkpoint. The measure of success here was capability and trustworthiness rather than a percentage: does the workflow hold together, and can the operator see enough to take responsibility for the output.

Hospital utilisation performance intelligence

Scanning thousands of dimensional combinations to explain why a clinical utilisation metric moved — as a readable story, not another dashboard.

In production Healthcare Read more Close

The problem

Dashboard proliferation had produced analytical paralysis for a private hospital group: too many reports, conflicting KPIs, isolated metrics, and no clear path from a number to an action. The analysts were capable, but by the time they had connected all the dots the business context had moved on.

How it works

The platform processes thousands of dimensional combinations to detect the patterns and anomalies worth attention, then generates intelligent alerts with a narrative explaining each one — organised as news-style stories rather than charts. Recommendations become tracked initiatives with named accountability, and an AI chat layer answers follow-up questions against the same curated diagnostic intelligence.

The value case

Move from monthly retrospective reporting to spotting shifts sooner and explaining the why in language an executive can act on. It builds on an existing diagnostics platform already adopted by the group, which is what makes the incremental value assessable.

Proactive financial intelligence

A daily agent workflow that watches the numbers, investigates what changed, and emails the owner what to do about it.

In production Financial services Read more Close

The problem

Businesses have comprehensive financial data and no time to convert it into decisions. Problems surface through the monthly cycle — weeks after they could have been addressed — root-cause work needs manual effort or outside consultants, and finance teams lose a large share of their week to data preparation rather than analysis.

How it works

Four steps run daily without being asked: access the accounting data through a secure connection; observe cash position, revenue patterns and expense variances against months of history; analyse what caused a detected change, combining accounting data with captured business context; and act by recommending specific, implementable next steps. An orchestrator coordinates specialised agents, with a sandboxed Python environment for the calculations and tenant isolation throughout.

The value case

Return the 15–20 hours a week that financial decision-makers spend on aggregation and analysis, and shorten the gap between a problem occurring and someone knowing about it. The hours figure is the baseline established with the client at scoping, and it is what the deployment is being measured against.

Pattern two

Optimise

A decision with many valid answers, hard constraints, and trade-offs that are genuinely difficult to hold in one head — cost against emissions against a standard that must not be breached. These agents search the option space and show their reasoning, so the engineer or planner keeps the judgement call.

Cementitious mix optimisation

Recipe design balancing compressive strength, CO₂ and production cost while keeping standards compliance intact.

In prototype Building materials Read more Close

The problem

Recipe development meant evaluating oxide compositions across multiple suppliers, testing combinations against the governing European standard, and balancing 28-day strength targets against CO₂ and cost. Every cycle was long, only a fraction of the possible combinations ever got tested, and systematic exploration of the environmental trade-off was impractical.

How it works

The agent predicts 28-day compressive strength, generates candidate recipes from the full catalogue of available materials, analyses substitutions, and optimises for minimal CO₂, minimal cost or a stated balance of the two — enforcing standards compliance as a constraint rather than checking it afterwards.

The value case

Turn an expert-dependent formulation process into a repeatable, data-driven one: faster supplier decisions, compliant recipes in minutes, and a shorter route to market for new supplementary cementitious materials. Targets set at scoping include a materially shorter development cycle and a reduction in embodied CO₂ per formulation.

Concrete strength optimisation

Multi-plant recipe optimisation across thousands of catalogue recipes and years of production batches.

In prototype Building materials Read more Close

The problem

Recipe development and quality assurance required manually decoding mix design parameters and adjusting cement and water ratios to hit strength targets, while balancing CO₂ and cost across several plants at once. The workflow was slow, performance varied between batches, and over-specified cement content left both cost and emissions savings on the table.

How it works

The agent decodes mix design identifiers, predicts 7-, 28- and 91-day compressive strength, forecasts CO₂ and cost per cubic metre, and runs an evolutionary optimisation across water, cement, aggregates and admixtures — respecting exposure-class constraints and plant-specific conversion equations. It is grounded in a catalogue of several thousand recipes across seven plants and roughly fifteen thousand historical batches.

The value case

Remove the trial-and-error loop and give plant teams immediate, constraint-compliant recommendations backed by their own production history. The target is a substantially shorter optimisation cycle and reduced over-specification of cement; per-cubic-metre savings were modelled with the client and are commercially sensitive.

Production planning and scheduling

Two-week sequenced schedules across three plants and twelve sub-teams, with rapid replanning when the day goes wrong.

In design Building materials Read more Close

The problem

Planning across three plants and twelve sub-teams was manual and time-intensive. Element batching was suboptimal, setup changes were more frequent than they needed to be, and replanning around sick leave or a late material delivery was slow enough that the disruption had already cost something by the time the new plan existed.

How it works

The agent optimises element batching and setup time, generates sequenced two-week schedules with delivery ordering, and replans quickly when the inputs change during the day. Planners see the whole schedule rather than their own slice of it.

The value case

Cut the planning effort substantially, reduce the number of costly mould setup changes, and protect delivery dates with genuine buffer instead of optimism. This agent is at design stage: the figures discussed with the client — a large reduction in planning time and a double-digit percentage saving in setup — are modelled expectations, not observed results.

Clinker quality and consistency stabilisation

Learning from historical process data to react to off-spec clinker before it becomes a penalty or a dispute.

In feasibility testing Building materials Read more Close

The problem

Off-spec clinker is expensive in three directions at once: penalties, customer disputes and lost revenue. Reaction time depends on someone noticing a drift in the process and correctly attributing it, which historically meant the problem was identified after the product was already out of specification.

How it works

The agent learns from the plant's historical process and quality data to recognise the conditions that precede off-spec output, so the control room gets a warning and a probable cause rather than a lagging quality result.

The value case

Improve reaction time to quality drift, and reduce the penalties, disputes and lost revenue that follow off-spec production. This is at feasibility stage — the value case has been articulated with the plant but not yet quantified.

Resource and contractor capacity optimisation

Predictive workforce and contractor allocation across a portfolio of mining capital projects.

Proof of concept Mining Read more Close

The problem

Resource optimisation across projects, regions and disciplines required navigating complex schedules and resource databases to assess capacity, find bottlenecks and build an allocation strategy. In practice, decisions were reactive — made once a constraint had already surfaced, which produces last-minute scrambling and poor allocation across the portfolio.

How it works

The agent analyses workforce constraints and contractor capacity against project schedules to identify where the portfolio is heading for a bottleneck, and evaluates allocation options against timelines and capacity limits before the constraint bites.

The value case

Shift resource planning from reactive to predictive, and give portfolio leadership a defensible basis for contractor engagement decisions. Scoped as a proof of concept: the outcome being tested is whether the analysis is trustworthy enough to plan against.

Pattern three

Extract

The information exists — in a drawing, a contract, a photograph, a statement, a pitch deck — and getting it into a usable structure is slow, repetitive and error-prone. These agents do the reading and the transcription, with confidence scoring and escalation when the evidence is genuinely ambiguous.

Bill of resources from engineering drawings

Reading precast concrete drawings to determine the material and labour a job actually needs.

In feasibility testing Building materials Read more Close

The problem

Extracting a bill of resources from engineering drawings took four to eight hours per element type and still produced calculation errors in the range of 15–35%, along with overlooked components and quantity take-offs that differed between estimators. The downstream cost is material ordering errors, budget overruns and delay.

How it works

The agent identifies production data and specification tables in the drawing set, distinguishes hollow-core from solid elements, detects combined components, performs net-volume calculations, and validates measurements against manufacturer data before assembling a multi-sheet bill of resources.

The value case

Take the preparation from hours to minutes and remove the arithmetic error class entirely, so procurement works from a quantity take-off that does not need re-checking. The error range above is the measured manual baseline; the time reduction is the target the agent is being built against.

Visual classification of recycled materials

Classifying incoming truck loads from a single overhead photograph, with evidence and a confidence score.

In feasibility testing Building materials Read more Close

The problem

Operators at a recycled-materials reception network photographed incoming loads and assigned product codes by eye across ten material categories spanning demolished concrete, brick, soil and rock. Classifications differed between shifts and sites, loads were routed into the wrong product streams, and material that could have been sold as higher-value product was written down as generic waste.

How it works

The agent works through fifteen structured visual checkpoints on the overhead photograph, applies site-specific eligibility rules across eleven reception sites, and matches what it observes against a ten-class product fingerprint table. It returns a product name and code with an audit-ready confidence score, and escalates a load for manual review only when the evidence is genuinely ambiguous.

The value case

Replace inconsistent visual inspection with a repeatable, evidence-based process: less misrouting, more sellable material recovered, and a fast routing decision the operator can defend. Classification accuracy and decision-time targets were set with the client against the current manual process.

Spend reclassification

A unified, self-improving view of fragmented procurement spend for a global consumer goods business.

Piloted in four markets Manufacturing Read more Close

The problem

Spend data sat across multiple systems with transactions classified inconsistently, which undermined budget allocation at group level. Manual reclassification was slow and produced different answers depending on who did it, hiding both supplier-consolidation opportunities and value leakage.

How it works

Contracts, purchase orders, invoices and general-ledger data are ingested into a unified layer. Spend attributes are enriched from external datasets and web search, then an ensemble of machine-learning models classifies the transactions, with a feedback loop so corrections improve subsequent runs.

The value case

Give procurement one accurate view of spend to source strategically against, and quantify the misclassification that had been distorting budget decisions. Proven first as a proof of concept, then piloted across four markets with scaling decisions taken on the pilot evidence — the sequence matters more here than any single percentage.

Contract term extraction and leakage detection

Turning a fragmented contract portfolio into structured, benchmarkable terms with automated alerts.

Proof of concept Manufacturing Read more Close

The problem

Contracts were spread across disparate systems, making compliance at scale expensive to maintain. Manual review could not keep pace, so rate differences between comparable suppliers and untracked commodity and foreign-exchange movements quietly eroded margin.

How it works

The agent extracts critical terms into a structured format so rate cards can be benchmarked against comparable suppliers, identifies price and payment-term leakage, and tracks commodity and FX movements with automated alerts rather than a quarterly look-back.

The value case

Move contract oversight from periodic and partial to continuous, and surface leakage while there is still time to act on it. Developed as a proof of concept alongside the spend work and shortlisted for piloting; the exposure it identifies is client-confidential.

Payment term benchmarking

Systematic detection of unfavourable terms, system-versus-contract mismatches and early payments across the supply base.

Proof of concept Manufacturing Read more Close

The problem

Contracted payment terms frequently differed from the terms captured in operational systems, and early payments were eroding the cash position. At the scale of a global supply base, manual tracking could not produce portfolio-wide visibility, so working capital was managed on partial information.

How it works

The agent benchmarks payment terms across the supply base, flags unfavourable terms and contract-to-system mismatches, identifies early payments and deviations, and recommends actions with a simulation of the outcome so the trade-off is explicit before anyone renegotiates.

The value case

Convert working capital from something reported after the fact into something managed, with a specific, prioritised list of contracts worth renegotiating. Built as a proof of concept and put forward for piloting.

Invoice and supplier statement reconciliation

Reconciling supplier statements and invoice correspondence against the ERP, with a complete audit trail.

In design Building materials Read more Close

The problem

Manual reconciliation reached only a fraction of the supplier base each month. That delayed month-end close and left accruals resting on estimates, with the unreconciled remainder representing unquantified risk rather than a known position.

How it works

The agent processes statements and invoice correspondence, matches them against the finance system, identifies discrepancies with the evidence attached, and produces a report with recommended actions and a full audit trail for the auditors to follow.

The value case

Expand monthly reconciliation coverage by an order of magnitude and shorten close, so accruals rest on reconciled data rather than judgement. The coverage target was set with the group's finance function; the agent is at design stage.

Pitch-deck extraction for deal onboarding

Reading a founder's pitch deck or investor memo and pre-filling the application, leaving the founder to correct rather than type.

In production Financial services Read more Close

The problem

A deal marketplace connecting founders with funders depended on founders completing a long structured application by hand. The information already existed in the pitch deck they had just uploaded, and every field they had to retype was a point at which an application was abandoned.

How it works

When a pitch deck or investor memo is uploaded, the assistant extracts the application data points and auto-populates the form, leaving gaps where information genuinely is not present. The founder edits and confirms the extracted values before submitting, so accuracy stays with the person who knows.

The value case

Shorten founder onboarding and reduce drop-off, which directly increases usable deal flow reaching funders. Deployed as part of the platform's live build; the outcome the client tracks is completed applications, not extraction accuracy in isolation.

Pattern four

Assure

A document is about to move to a committee, a stage gate or a signature, and the review standing between it and that decision is inconsistent, slow or dependent on one overloaded expert. These agents apply the same criteria every time and say plainly what is missing.

Capital proposal review

Automated quality, compliance and language review of several thousand capital proposals a year before they reach committee.

In production Building materials Read more Close

The problem

Roughly four thousand capital proposals a year arrived with uneven template adherence, inconsistent content quality and variable language standards. Executive time went into deciphering submissions rather than evaluating them, and capital prioritisation decisions were delayed by avoidable back-and-forth.

How it works

The reviewer assesses each proposal for template adherence, content quality and language, returning red-amber-green graded feedback with specific recommendations. Submitters iterate against it before submission, and the committee receives a standardised assessment scorecard alongside the proposal.

The value case

Higher-quality submissions reaching the committee faster, with executives spending their time on substantive evaluation rather than editing. The intended outcome is better and quicker capital prioritisation — deliberately expressed as an outcome, because proposal quality is not usefully reduced to a single percentage.

Engineering deliverable maturity assurance

Rating engineering deliverables on completeness, quality, integration and stability to test genuine stage-gate readiness.

In production, expanding Mining Read more Close

The problem

Deciding whether an engineering deliverable is ready for a stage gate depends on more than whether it looks complete. Reviews were manual, one document at a time, and could not reliably surface whether accepting a deliverable would force rework elsewhere, or whether its key assumptions were actually closed out.

How it works

The agent assesses a deliverable across four pillars — completeness, quality, integration and stability — and combines them into a single maturity rating with factor scores, risk-rated findings, the evidence reviewed and recommended actions. The integration assessment evaluates the deliverable against interfacing deliverables, disciplines and project decisions; the stability assessment tests whether assumptions are validated, decisions closed and external dependencies such as geotechnical, vendor, testwork and regulatory inputs resolved.

The value case

Surface residual technical and project risk that a document-by-document review cannot detect, so stage-gate decisions are made on a complete picture. The first phase covered six of forty-two identified engineering deliverables; current work extends the library, adds multi-deliverable assessment and lets the agent talk directly to the governance agents instead of a person copying results between them.

Project charter quality assessment

Scoring each section of a project charter against defined completeness and quality criteria before it enters approval.

In development Mining Read more Close

The problem

Charter review was ad hoc. Whether gaps and thin sections were caught depended on who reviewed the document and how much time they had, which allowed incomplete charters to progress through approval and create problems later in the project.

How it works

The agent works through each section of an uploaded charter against defined completeness and quality criteria, producing a structured score and naming the gaps, the areas with insufficient detail and the points needing clarification — practical guidance the submitter can act on before approval.

The value case

A consistent, objective charter review that both submitters and approvers can trust, and fewer incomplete charters progressing. Currently in development.

Investment committee preparation

Identifying risks, red flags and the critical questions to put to project heads ahead of an investment review.

In development Mining Read more Close

The problem

Preparing for an investment committee review meant a committee member reading extensive project documentation to work out where the risks were and what to ask. The depth of that preparation varied with available time, and the quality of the questions asked varied with it.

How it works

Connected to the investment knowledge libraries and the submitted request, the agent systematically identifies potential risks, red flags and the grounds that could justify declining — then generates a prioritised list of the critical questions to put to project heads.

The value case

Better-informed go/no-go decisions from a more consistent evaluation, with committee members arriving prepared without days of manual reading. In development; the outcome is decision quality rather than time saved.

Investment criteria evaluation

Automating evaluation of investment requests against the group's investment criteria framework.

In production Mining Read more Close

The problem

Determining which disciplines and criteria applied to a given investment request, and evaluating against them, was manual and interpretive. Different reviewers reached different conclusions from the same documents, which made portfolio-level comparison unreliable.

How it works

The framework documentation is processed into a searchable knowledge base behind a purpose-built retrieval service. The agent extracts the key characteristics of an uploaded project, determines which disciplines and criteria apply based on its scope, and evaluates the request against them systematically.

The value case

Repeatable, consistent investment evaluation across a capital portfolio, so a comparison between two projects reflects the projects rather than their reviewers. Part of a suite now in production and under active maintenance.

Due-diligence data room experts

A dedicated, isolated due-diligence expert per deal, running six standard analysis and drafting workflows.

In production Financial services Read more Close

The problem

Due diligence on early-stage deals is expensive relative to cheque size, which means it is often done thinly or late. Documents sat in a data room with no analytical layer, so every funder repeated the same reading from scratch.

How it works

Each deal gets its own isolated expert with strict data segregation. Documents uploaded to the data room synchronise to it automatically across common file formats, and six workflows are configured and ready: financial analysis, desktop analysis, reputational risk assessment, investment document drafting, legal document drafting and legal document review. Access is controlled per participant with a full audit trail.

The value case

Make thorough diligence economic at small deal sizes, and give funders standardised reports and drafts instead of a folder of PDFs. Live in the platform as a subscription feature — the commercial signal is that participants pay for it.

Pattern five

Advise

The organisation already knows the answer — it is in a procedure, a guideline, a standard, or the head of someone who is busy. These agents put that expertise next to the person doing the work, at the moment they need it, with the source attached so it can be checked.

Safety leadership and hazard identification experts

Two agents guiding site safety conversations and systematic hazard identification, replacing paper.

In production Building materials Read more Close

The problem

Safety processes ran on paper and depended on individual interpretation. Safety leadership interactions lacked structured guidance, so their quality varied with the leader conducting them, and hazard identification was incomplete in ways that only became visible after an incident.

How it works

A safety leadership interaction expert provides site-specific preparation and recommendations, then automates the post-interaction logging that previously went onto a form and into a drawer. A hazard identification expert guides the user systematically through risks, drawing on operational data and interpreting photographs of the actual work area.

The value case

Turn inconsistent paper activities into structured, interactive ones: better prepared leaders, more complete hazard coverage, and a record that supports compliance. The intended outcome is worker safety, which is the reason it is expressed as coverage and consistency rather than a percentage.

Conversational access to standard operating procedures

Asking a procedure a question instead of searching a document library, in a regulated manufacturing environment.

In production Manufacturing Read more Close

The problem

A global healthcare manufacturer's SOP system was hard to use and heavily documented, which produced information overload and, more seriously, a compliance risk: when finding the right procedure is slow, people work from memory instead.

How it works

The agent provides conversational access to procedures with the source referenced, and generates RACI matrices from the procedure content so responsibility is explicit rather than inferred.

The value case

Take SOP access from minutes of searching to seconds of asking, so the documented procedure is the path of least resistance. This agent was built during an ongoing engagement rather than under a discrete scope, so its value case is qualitative: procedure adherence and reduced compliance exposure.

Commercial research and sales expert

Researching a prospect and mapping their needs against the full service catalogue before a logistics sales conversation.

In production Logistics Read more Close

The problem

Pre-sales preparation in a large logistics business involved significant manual effort, and the breadth of the group's own capability worked against it: no individual salesperson could hold the entire offering in mind, so cross-sell opportunities that existed on paper never surfaced in the conversation.

How it works

The expert performs deep client research, systematically maps the prospect's likely needs against the full range of group capabilities, and recommends the specific slides and sections to assemble into a tailored pitch. It surfaces non-obvious connections a specialist in one service line would not think to look for.

The value case

Less time preparing and more time in front of clients, with higher-quality, genuinely tailored pitches — and, over time, more deals closed per quarter. Users are explicitly expected to review and apply judgement to what the agent produces; it augments the seller rather than replacing their read of the account.

Strategic project review and guidelines compliance

The two agents that started an enterprise suite: interrogating monthly project reporting, and answering questions against the project management framework.

In production Mining Read more Close

The problem

A global miner managed substantial capital portfolios through 200-page monthly reports that carried data but little narrative and few actionable insights. Executives could not extract a decision from them quickly, and guideline compliance depended on knowing which of eighteen framework guidelines applied.

How it works

One agent ingests the monthly reporting each cycle and supports strategic project review conversations against it. A second is grounded in the project management framework, guidelines and lessons-learned documentation, answering compliance and interpretation questions with the source referenced. Both are maintained on an ongoing basis as the underlying documents change — the monthly reports are re-provisioned every cycle.

The value case

Move executive review from hours of manual analysis to minutes of interrogation, and make guideline interpretation consistent. These two were proof-of-concept agents whose results justified a suite that now spans project review, guidelines, investment criteria, engineering standards and contract evaluation.

Cross-framework guidance retrieval

A rebuild for questions that span several governance frameworks at once, rather than drilling into one.

In development Mining Read more Close

The problem

Usage of the guidelines agent revealed a demand its architecture was not built for: people ask questions that draw on project management guidelines, investment criteria, decision-management documents and value-share material simultaneously. It could accommodate some of that, but accuracy and reliability degraded outside the vertical scope it was designed for — a limitation worth fixing properly rather than papering over.

How it works

The agent is being rebuilt purpose-designed for horizontal, multi-source querying across the framework landscape, so a single question returns an integrated, source-referenced answer reflecting the full breadth of applicable guidance rather than the corner of it that was searched.

The value case

A dependable single point of enquiry for governance questions, which is what determines whether people use the framework or work around it. In development; this one exists because observed usage contradicted the original design assumption.

Technical documentation retrieval

Conversational access to a technical development knowledge repository, with sources cited.

In development Mining Read more Close

The problem

Getting to the right technical information depended on knowing where to look or whom to ask. That slows teams down, produces inconsistent application of documented standards, and quietly turns a knowledge base into an archive nobody consults.

How it works

The agent is connected to the technical development repository and answers questions conversationally, identifying the relevant documentation and extracting precise, source-referenced answers — no manual search, no interrupting a colleague.

The value case

More consistent application of documented standards, and a knowledge base teams actually rely on rather than one that ages out of relevance. In development.

HR query and response agent

Answering employee HR questions with the source quoted, and drafting the reply for a person to send.

In user acceptance testing Building materials Read more Close

The problem

A corporate HR team answered a steady volume of questions whose answers were already documented in policy. Each one still needed someone to locate the policy, interpret it and write a reply — repetitive work that displaced the HR work only people can do.

How it works

The agent answers HR queries, quotes the source it relied on, and drafts an email reply for a team member to review and send. The human stays in the loop on anything going out to an employee.

The value case

Return time to the HR team and make answers consistent regardless of who fields the question. In user acceptance testing with the team who will use it.

Agent directory and onboarding guide

An agent whose job is helping people find the right agent — the adoption problem that arrives once a suite succeeds.

In development Mining Read more Close

The problem

A growing suite creates a problem of its own: as the library expands, navigating it becomes a barrier. Agents go undiscovered and training material goes unused, which means the investment in each individual agent is only partly realised.

How it works

Configured as the entry point to the suite, this agent interprets what the user is trying to do and directs them to the relevant agent, or surfaces the right training resource directly in the conversation. No prior knowledge of how the platform is organised is required.

The value case

Faster onboarding and stronger adoption across the suite — the difference between agents that exist and agents that get used. In development. We include it because adoption is where most enterprise AI programmes actually fail.

Pattern six

Forecast

Recurring numbers that must be produced on a cycle, reconciled, scenario-tested and packaged for people who will make decisions on them. Highly procedural, unforgiving of error, and usually held together by one person and a spreadsheet.

Cash flow forecasting

End-to-end monthly forecasting across ten legal entities in three countries, with reconciled base, downside and upside scenarios.

In production Building materials Read more Close

The problem

A Central European building materials group consolidated actuals from ten legal entities across three countries by hand each month, reconciled ageing reports, and applied scenario assumptions through hundreds of formula touchpoints. It was slow, exposed to human error at every touchpoint, and applied methodology inconsistently between entities and between cycles.

How it works

The agent retrieves the source files directly, identifies the latest actual month, applies ageing-based trade debtor and DPO-driven trade creditor methodologies, and generates three fully reconciled scenarios — base, downside and upside — across every entity and consolidation level, producing audit-ready Excel and PDF output without manual intervention.

The value case

Turn a multi-day cycle into a run, eliminate the formula error class, and give finance leadership consistent, traceable scenarios every month rather than a differently-built model each time. This agent was built inside a broader programme rather than under a discrete scope, so treat the time figures as the client's own working baseline.

Temperature mapping analysis

Automating log processing, analytics and report guidance for temperature mapping in a regulated environment.

In production Manufacturing Read more Close

The problem

Temperature mapping consumed substantial staff time in a healthcare manufacturing setting, carried real error risk, and the manual method limited how deeply anyone could analyse the data. In a regulated context, that combination is a compliance exposure as much as an efficiency one.

How it works

The agent automates log processing and the analytics over it, and guides the preparation of the report that regulators and auditors will read — so the depth of analysis no longer depends on how much time the analyst had.

The value case

Shift the analysis from manual to automated, improve accuracy and cut report compilation time, while enabling the root-cause analysis that compliance actually requires. Built during an ongoing engagement; the value case is qualitative.

Commodity price index forecasting

AI-driven price index predictions to support commodity hedging decisions in global procurement.

Proof of concept Manufacturing Read more Close

The problem

Hedging decisions in a global procurement function depend on a view of where commodity prices are heading. Without a systematic forecast, timing decisions rest on judgement and whatever market commentary is to hand, and the cost of being wrong is carried across every operating company.

How it works

A forecasting model produces commodity price index predictions to inform hedging timing, developed alongside the contract and payment-term work as part of a single procurement AI roadmap rather than as an isolated tool.

The value case

Better-timed hedging against a systematic forecast instead of ad hoc judgement. Developed as a proof of concept and put forward for piloting — the honest status is promising and unproven at scale.

Revenue cycle metrics analysis

Calculating and interpreting core revenue-cycle metrics for a healthcare partner, querying their own document estate directly.

In development Healthcare Read more Close

The problem

Revenue cycle management analysis depends on a defined set of metrics calculated consistently from data spread across internal document stores. Doing that by hand each cycle is slow, and inconsistency in the calculation undermines any comparison over time.

How it works

The agent carries built-in metric definitions and calculation scripts, and queries the client's internal document folders directly through a secure data access layer. It was handed over with training and knowledge transfer so the client's own team can adapt and extend it.

The value case

Consistent, repeatable revenue-cycle metrics without a manual assembly step, owned and extended by the client's team rather than dependent on us. In development.

Where to start

Bring us a workflow, not a technology question

The agents on this page began as high-friction workflows, recurring decisions or processes where quality, speed or control had to improve. If one of the six patterns above resembles something in your organisation, that is usually the fastest route to a value case worth testing.

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