01
Nobody trusts an unverifiable number
An extracted figure that cannot be traced to its source is worse than no automation, because it introduces confident error.
Industry
AI & Data Platforms
Products whose value comes from turning unstructured input into a structured, trustworthy answer — document extraction, categorisation, research tooling and analytical interfaces.
- Sector
- AI & data platforms
- Projects delivered
- Architecture ready
- Pressures
- 4 core constraints
- Capabilities
- 3 disciplines
Sector Delivery Scope
4 Specialized Systems
Architectural Modules
- Ingestion and normalisation
- Extraction and classification
- Analysis interfaces
Operational Constraints
What makes ai & data platforms hard.
Automation you can verify, on data you can trace
02
Document variance defeats naive parsing
Real inputs vary by issuer, format and quality. A pipeline that works on samples usually fails on the long tail.
03
Model providers move
Pricing, availability and capability change quickly, so model choice must not be welded into the application.
04
Data residency is a constraint
Where data legally cannot leave a jurisdiction, provider selection is a design decision, not a procurement one.
Architecture & Systems
The systems that answer those pressures.
System 01PRODUCTION SCOPE
Ingestion and normalisation
Pipelines that turn inconsistent source formats into a single schema, retaining provenance per record.
System 02PRODUCTION SCOPE
Extraction and classification
Model-assisted extraction behind a swappable provider interface, with confidence thresholds and human review.
System 03PRODUCTION SCOPE
Analysis interfaces
Graph and dashboard views that make relationships legible, plus generated documents derived from the same data.
System 04PRODUCTION SCOPE
Evaluation harnesses
Held-out test sets and accuracy measurement, so quality is a tracked number rather than an impression.
No published case study in this sector yet.
We are honest about where the portfolio is thin. The method, the stack and the engagement models are the same as the work shown elsewhere on this site — what we cannot show you is a public reference in ai & data platforms specifically.
Method
Five phases, adapted to the sector's risk.
- 01
Phase 01: Diagnose
1 — 2 weeksWe look at the actual system, the actual data and the actual workflow before proposing anything. Output is a written assessment: where the constraint really is, what it costs you today, and the options ranked by cost and risk.
Outputs
4 deliverables- Technical and process assessment
- Constraint identified and quantified
- Options with cost, timeline and risk
- A recommendation we are willing to defend
- 02
Phase 02: Architect
1 — 3 weeksDomain model, route and data architecture, integration boundaries and non-functional requirements agreed in writing. This is the cheapest place to be wrong, so we spend the time here rather than discovering it in month four.
Outputs
4 deliverables- Domain and data model
- Architecture decision record
- Integration and API contracts
- Performance budget and acceptance criteria
- 03
Phase 03: Build
4 — 16 weeksTwo-week cycles against a live environment you can open at any time. No screenshot theatre — you review working software with realistic data, and priorities can be reordered between cycles.
Outputs
4 deliverables- Working software every two weeks
- Live staging environment from week one
- Cycle reviews with a written summary
- Visible backlog with priority agreed with you
- 04
Phase 04: Harden
1 — 3 weeksThe unglamorous work that decides whether launch is calm: permissions reviewed, error paths exercised, performance measured against the budget, migrations rehearsed and reversed.
Outputs
4 deliverables- Security and permission review
- Performance and load verification
- Migration rehearsal with rollback
- Monitoring, alerting and error tracking
- 05
Phase 05: Operate
OngoingLaunch is a checkpoint, not an ending. Under a retainer we watch real traffic, fix what the data exposes, and keep shipping the next increment of value.
Outputs
4 deliverables- Production monitoring and incident response
- Iteration on measured behaviour
- Documentation kept current
- Handover to your team whenever you choose