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.

At a glance

Agents in this library: 37
Sectors: 7

How this is organised

Thirty-seven agents, six kinds of job

Take the industry away and nearly all of these agents are doing one of six jobs: working out why something happened, choosing the best option under real constraints, reading documents into structured data, checking work against a standard, answering from what the organisation already knows, or producing a number on a cycle.

Those six recur because the underlying job recurs. A process engineer diagnosing a kiln stoppage and a finance lead explaining a cash movement need the same shape of agent, even though nothing else about their work looks alike. So the library is grouped by capability — the job the agent does — rather than by industry. If you would rather see it by sector, switch to the Industry view.

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

Group by

Sector

Figures shown as a target are business cases agreed with the client during scoping — the benchmark the work is held to, not an audited post-deployment result. Figures shown as scale describe the environment the agent operates in. Clients are described rather than named.

Root cause analysis for kiln stoppages

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

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.

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.

Financial transaction anomaly detection

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

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.

Multi-agent maintenance diagnostics

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

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

Show 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 is 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.

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.

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 time financial decision-makers spend on aggregation and analysis, and shorten the gap between a problem occurring and someone knowing about it.

Mix design for new cementitious materials

Shortening the qualification cycle between a newly available supplementary cementitious material and a concrete mix that can actually be sold.

Building materials Read more Close

The problem

When a new supplementary cementitious material becomes available, getting it into a saleable concrete mix means evaluating its oxide composition, testing combinations against the governing European standard, and proving strength at 28 days. Every cycle is long and lab-bound, only a fraction of the possible combinations is ever tested, and the trade-off against CO₂ is never explored systematically — so a material that could reduce the carbon of a mix waits behind the testing queue.

How it works

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

The value case

What the client is after is time to market: a shorter route from “this material is available” to “this mix can be sold”. Faster supplier decisions and a systematic view of the carbon trade-off come out of the same work.

Concrete strength prediction and setpoint optimisation

Predicting concrete quality from mix and process data so a plant can tune its operational setpoints instead of over-specifying cement.

Building materials Read more Close

The problem

Hitting a strength target reliably meant decoding mix design parameters by hand and adjusting cement and water ratios on experience. Performance varied between batches, and the safe response to that variation is to put in more cement than the specification needs — which costs the material itself and the energy embodied in producing it, on every cubic metre.

How it works

The agent predicts concrete quality from the mix design and production data, then optimises the operational setpoints that determine it — across water, cement, aggregates and admixtures — while respecting the exposure-class constraints the finished product has to meet.

The value case

Less material and less energy per cubic metre, by removing the trial-and-error loop and the habitual over-specification that compensates for it. Per-cubic-metre savings were modelled with the client and are commercially sensitive, so they are not published here.

Production planning and scheduling

Sequenced production schedules across a multi-plant precast operation, with rapid replanning when the day goes wrong.

Building materials Read more Close

The problem

Planning across multiple plants and their 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 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 effort in building and rebuilding a plan, reduce the number of costly setup changes, and protect delivery dates with genuine buffer instead of optimism. The value case agreed with the client rests on reduced re-planning time and the opportunity cost that follows from it.

Clinker quality and consistency stabilisation

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

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. The value case has been articulated with the plant but not yet quantified — which is why no figure appears here.

Resource and contractor capacity optimisation

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

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. The outcome being tested is whether the analysis is trustworthy enough to plan against.

Bill of resources from engineering drawings

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

Building materials Read more Close

The problem

Determining the material and labour a precast job needs meant an estimator working through the drawing set by hand: slow, prone to arithmetic error, and inconsistent between estimators. Overlooked components and quantity take-offs that differ between people turn into mispriced offers, material ordering errors 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 an offer can be built quickly from a quantity take-off that does not need re-checking.

Visual classification of recycled materials

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

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.

Spend reclassification

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

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. Scaling decisions were taken on pilot evidence rather than on a single headline percentage.

Contract term extraction and leakage detection

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

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 alongside the spend work as part of a single procurement roadmap; 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.

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.

Invoice and supplier statement reconciliation

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

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.

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.

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. The outcome the client tracks is completed applications, not extraction accuracy in isolation.

Capital proposal review

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

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.

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. Current work extends the deliverable 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.

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 to approval.

Investment committee preparation

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

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. The outcome here is decision quality rather than time saved.

Investment criteria evaluation

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

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 broader governance suite under active maintenance.

Due-diligence data room experts

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

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. Offered in the platform as a subscription feature — the commercial signal is that participants pay for it.

Safety leadership and hazard identification experts

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

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.

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 inside 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.

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.

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 came first, and their 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.

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. 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.

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.

Multilingual enterprise knowledge search

Scaling document search from a pilot to a global workforce in several languages, with a secure instance for due diligence.

Mining Read more Close

The problem

A mining group's first-generation knowledge search worked, on a subset of documents, for a pilot group of English-speaking users. Scaling it globally meant confronting three things at once: the workforce is multilingual, due-diligence material cannot sit in a general-access system, and the document estate is far larger than the pilot's.

How it works

The capability moved onto the enterprise platform, adding Bulgarian, Serbian and Spanish, extending across additional document libraries, and introducing a separately secured instance for due-diligence work with access governed through the corporate directory. Administrators retain oversight, and users can rate answers so retrieval quality keeps improving.

The value case

Extend the benefit of the pilot to as much of the organisation as possible, in the languages people actually work in, without weakening the controls that sensitive material requires. The measure is breadth of adoption across divisions rather than a time saving per query.

HR query and response agent

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

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. Built with the team who use it, and deliberately kept human-in-the-loop for anything an employee will read.

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.

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. We include it because adoption is where most enterprise AI programmes actually fail.

Cash flow forecasting

End-to-end monthly forecasting across a multi-entity, multi-country group, with reconciled base, downside and upside scenarios.

Building materials Read more Close

The problem

A Central European building materials group consolidated actuals from each of its legal entities by hand every 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.

Temperature mapping analysis

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

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 inside an ongoing engagement, so the value case is qualitative.

Commodity price index forecasting

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

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. The honest position is promising and unproven at scale — which is why there is no figure attached.

Revenue cycle metrics analysis

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

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.

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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