AI use-case delivery
Production AI use cases,
not throwaway pilots.
Nine use cases we deliver as operational capabilities: scoped against your data in a discovery week, built with tests and monitoring, deployed to your infrastructure with governance included, and handed over so your team can run them. Fixed-fee, 4–16 weeks.
Catalog
Nine use cases, one delivery template
Every delivery follows the same shape: a short discovery to validate your data supports the outcome, a fixed-price build with demo checkpoints, and production handover with monitoring, documentation and training. Outcomes below are engagement targets we scope and measure against — agreed with you before the build starts.
Contract & document intelligence
- The problem
- Teams spend a large share of their week reading contracts, reports and filings for the handful of clauses and figures that actually matter — and miss things under time pressure.
- What we deliver
- A pipeline that ingests your documents, extracts the terms and risk clauses you define, and produces structured summaries with citations back to the source passage — deployed inside your perimeter, so documents never leave.
Target: review time cut by well over half, with a consistent, auditable risk read on every document.
Typical timeline: 6–8 weeks
Invoice & receipt processing
- The problem
- AP teams keying thousands of invoices a month produce error rates, duplicate payments and missed early-payment discounts that compound quietly.
- What we deliver
- Layout-aware extraction of line items, supplier matching against your master data, validation rules that mirror your policies, and posting into your ERP or accounting system.
Target: most invoices processed without a human touch; exceptions surfaced with reasons, not silently dropped.
Typical timeline: 4–6 weeks
Enterprise knowledge search (RAG)
- The problem
- Your organisation’s knowledge is spread across drives, wikis, tickets and inboxes. People spend hours a week searching — or re-answer questions someone already answered.
- What we deliver
- A retrieval-augmented search assistant over your content: hybrid vector + keyword retrieval with reranking, answers with citations, access-control-aware so people only see what they’re allowed to see.
Target: one search box across every repository, answering with sources — and measurably less time lost to hunting.
Typical timeline: 8–12 weeks
Customer support automation
- The problem
- Most support volume is repetitive, but answering it well needs institutional knowledge scattered across systems — so handle times stay high and answers vary by agent.
- What we deliver
- A grounded assistant over your knowledge base and product docs: agent-assist first (suggested, cited answers inside your helpdesk), self-service deflection where confidence is high.
Target: faster handle times and a meaningful share of repetitive enquiries deflected — with citations, never improvised answers.
Typical timeline: 6–10 weeks
Content generation & summarisation
- The problem
- Routine writing — meeting notes, report drafts, standard communications, product copy — consumes skilled hours on work that follows predictable patterns.
- What we deliver
- Generation pipelines grounded in your source documents and style guides, with templates, validation rules and a human review workflow. Grounding means drafts cite their sources.
Target: first drafts in minutes instead of hours, in your house style, with nothing invented.
Typical timeline: 4–6 weeks
Market & tender monitoring
- The problem
- Opportunities — tenders, filings, price changes, competitor moves — are published across dozens of sites in inconsistent formats. By the time you spot them manually, the window has narrowed.
- What we deliver
- Exactly what we run for ourselves: continuous extraction from the sources that matter to you, normalised into one stream, with AI matching against your interest profile and alerting. See it live on this site.
Target: relevant opportunities surfaced within minutes-to-hours of publication, scored for fit.
Typical timeline: 4–8 weeks
Data platform & pipeline modernisation
- The problem
- AI projects stall because the data underneath them is fragmented, undocumented and manually assembled. Analysts spend most of their time preparing data, not using it.
- What we deliver
- Ingestion pipelines (including hard web sources — our specialism), a layered raw-to-consumable data architecture, quality checks that block bad data instead of reporting it later, and vector indexing where AI retrieval is the goal.
Target: one governed platform your analysts and your AI use cases draw from — dramatically less preparation time.
Typical timeline: 8–16 weeks
Churn & demand prediction
- The problem
- At-risk customers are identified only when they cancel; demand is forecast in spreadsheets with error rates that drive both stockouts and dead inventory.
- What we deliver
- Classical ML done properly: models trained on your history with honest holdout validation, explainable feature attributions, scheduled scoring wired into your CRM or planning workflow, and drift monitoring.
Target: risk visible weeks earlier and forecast error meaningfully reduced — validated against your own historical data before anything goes live.
Typical timeline: 8–12 weeks
AI governance implementation
- The problem
- Regulatory requirements are converging — the EU AI Act’s high-risk obligations become enforceable in August 2026 — and most organisations can’t even list the AI systems already in use.
- What we deliver
- AI inventory and risk classification, model documentation, human-oversight workflows, decision logging and audit evidence — implemented as working software in your systems.
Target: a defensible governance posture where audit answers are queries, not scrambles.
Typical timeline: 6–12 weeks
Something adjacent but not listed — fraud triage, dynamic pricing, multilingual translation pipelines, bespoke agents? Ask. If we're not the right builders for it, we'll say so.
How delivery works
Discovery to production in five steps
Discovery
1–2 weeks. We validate your data supports the outcome and fix the success metrics. No data, no build — we tell you honestly.
Setup
Environment and pipelines provisioned in your infrastructure with security configured from the start.
Build
Models, retrieval and integration built against your data with evaluation gates — demos every two weeks.
Pilot
Staging deployment with your team using it on real work. Acceptance measured against the agreed metrics.
Handover
Production release, runbooks, training, and 90 days of support included. You own everything.
Which use case would move the needle for you?
Describe the workflow and the data you have. We'll tell you whether it's deliverable, what it would take, and a fixed price for discovery — usually the same day.