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"The model is wrong, and we cannot tell when."
Every output ships with retrieval citations, confidence, and an HITL accuracy figure measured on a domain eval set — not the demo number.
01 Service · Generative AI
Enterprise-grade generative AI. Built for production, not the sandbox.
Generative-AI systems for organisations where accuracy is non-negotiable and every output must be operationally defensible. Models grounded in your own corpus, governed end-to-end, and shipped with the retrieval lineage compliance can re-walk a year later.
Generative AI, on the record.
We design, build and operate generative-AI systems for institutions whose answers have to be defensible. Retrieval grounded in your own corpus. Agents orchestrated through the Actigen decision-loop. Domain-tuned models where the off-the-shelf accuracy will not clear the threshold.
Every system ships with the retrieval lineage that lets you trace each output to the source it came from, the evaluation harness that produced the accuracy figure, and the human-in-the-loop checkpoints the regulator will ask about. Compliance is not bolted on at the end — it is the spec on day one.
We do not ship demos. We ship the audit pack.
The questions clients arrive with.
Six briefs we hear repeatedly—and how we solve them. If one of these reads like your week, the conversation starts with a prototype, not a slide deck.
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"Our knowledge sits in PDFs nobody can search."
A grounded RAG corpus over your scans, contracts, regulator letters and microfilm. Retrieval moves from days to seconds, lineage intact.
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"Compliance won't sign off on a black box."
Lineage by default. Every prompt, retrieval, tool call and validation step written to an audit pack the reviewer can re-walk.
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"We have a pilot that won't scale to production."
Pilot to production in weeks, not months. Actigen orchestration, evaluation gates per release, runbooks and SLOs day-one.
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"Costs are unpredictable; latency is worse."
Model routing, caching and batched inference. Per-request budget caps and p95 latency targets enforced through LLMOps.
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"Our agents loop, retry, and lose the trail."
The Actigen Planner / Router / Executor / Validator loop — bounded reasoning, graceful fallback, and a full reasoning trace on every run.
Ten capabilities. One framework.
The full Generative-AI stack we build on, end-to-end. Every capability is exercised through the Actigen 2.0 decision-loop — so the lineage, evaluation and human-in-the-loop gates are the same regardless of which one you start with.
Retrieval-augmented generation
Production-grade RAG engineered over your proprietary data infrastructure—including unstructured PDFs, contracts, legacy records, and compliance documentation. Hybrid semantic search, native citation architecture, and domain-tuned reranking.
Agentic workflows
The Actigen Planner / Router / Executor / Validator architecture. Delivering bounded machine reasoning, deterministic retry-and-fallback logic, and complete execution traces for absolute audit transparency.
Document intelligence
Automated extraction, semantic classification, and structured schema generation across complex multi-script documents, handwritten records, and low-resolution scans—validated by human-in-the-loop controls.
Domain-tuned models
Custom engineering for high-stakes environments where base model performance falls short. Full supervised fine-tuning (SFT), LoRA/QLoRA parameter adaptation, and alignment through RLHF and DPO, bound by strict evaluation gates.
Knowledge assistants
Enterprise-grade internal copilots mapped directly to your operational policies, SOPs, and historical data. Featuring role-based access control (RBAC), context-aware retrieval, and verifiable source provenance.
Code & engineering copilots
Secure developer infrastructure optimised for your proprietary monorepos, infrastructure runbooks, and internal architecture docs. Automating code refactoring, test scaffolding, and code-review enforcement.
Multimodal pipelines
Unified text and computer vision pipelines engineered for claims processing, technical drawings, asset inspections, and specialised imagery. Delivering high-precision object detection and structured JSON payloads for downstream core systems.
Guardrails & red-teaming
Enterprise security layers including real-time PII redaction, prompt-injection defence, toxicity filters, and continuous adversarial red-teaming. A comprehensive AI risk register is delivered as a core engineering component.
Evaluation harnesses + HITL
Rigorous benchmarking using custom domain eval sets, golden datasets, regression test suites, and internal reviewer interfaces. Accuracy is validated against enterprise production metrics, not generic vendor benchmarks.
LLMOps
Operational infrastructure handling smart model routing, semantic caching, prompt versioning, and deep cost/latency observability. Enforcing strict p95 latency SLOs across your private cloud or ours.
“Every capability is delivered through the same loop. The only thing that changes between briefs is which gate the auditor is most interested in.”
Talk to an engineerThe tools we build with.
A working summary of what we ship into production today. We do not chase the leaderboard; we choose the model that holds up to the eval set and the regulator. Filter by layer to see what fits your stack.
Filter
/ AI
Models, frameworks, retrieval
Frontier APIs, open-weight models, retrieval & orchestration.
Anthropic Claude
OpenAI GPT
Google Gemini
Llama 3
Mistral
Hugging Face
LangChain
LlamaIndex
LangGraph
NVIDIA NeMo
Guardrails AI
Pinecone
Weaviate
Qdrant
pgvector
/ Frontend
Interfaces & experience
Conversational UIs, reviewer consoles, document workflows.
React
Next.js
Vue
TypeScript
Tailwind
Streamlit
/ Backend
Services, APIs, data
Orchestration, persistence and the contracts the auditor will read.
Python · FastAPI
Node.js
.NET
Go
GraphQL
PostgreSQL
Redis
Kafka
/ Cloud
Hosting, scale, governance
Deploy in your cloud or ours — sovereign, hybrid, or private.
AWS Bedrock
Azure OpenAI
GCP Vertex AI
NVIDIA NIM
Databricks
Snowflake Cortex
Kubernetes
Terraform
LangSmith
The numbers after the audit.
Outcomes measured the way the auditor measures them — on the eval set, after human verification, with the cost and latency we agreed at the start. Not the demo number. The real one.
- 85–94%
- Field accuracy after human-in-the-loop verification
- Days → seconds
- Time-to-answer across grounded retrieval workflows
- 40–70%
- Cost reduction versus manual processing
- 100%
- Audit trail with full extraction lineage on every output
Ranges reflect actual performance metrics delivered across recent enterprise engagements. Specific production targets are committed to in writing during the initial Discovery phase.
Get a free consultation
Start the conversation
Have a corpus that won’t sit still? Provide a representative sample of your data infrastructure, and our engineering team will deliver a structured, measurable pilot architecture proposal within five business days.
Three lenses. One discipline.
Our AI & Automation practice delivers production systems through three interconnected engineering workstreams—embedding localised intelligence directly into legacy software, building generative-AI systems anchored to your proprietary data corpus, and stabilising the underlying data architecture that anchors the entire infrastructure.
/ 01
AI-Embedded Applications
Intelligence built into the software you already run — recommendation, classification, prediction and decision support, surfaced where the work happens.
Read more / 02Generative AI Solutions
RAG, agentic workflows, document intelligence and domain-tuned models — grounded in your corpus, governed end-to-end, lineage by default.
Read more / 03Data-Driven Architecture
Pipelines, warehouses, lakehouses and governance — the schema, lineage and contracts the generative layer needs to remain trustworthy at scale.
Read moreWhere this is doing the work today.
A summary of the briefs we are running this year. Click through for the case studies and the eval-set numbers behind each row.
Hansard generation, archive search across decades of microfilm, legislative drafting copilot grounded in precedent.
RAG · Drafting
See
KYC summarisation, claims triage, contract abstraction, regulatory-letter response drafting under counsel review.
Extraction · Agents
See
Publishing & Historic Research
Handwriting recognition, structured indexing, rights-metadata generation, multilingual citation resolution.
HWR · Extraction
See
Built for institutions, not for demos.
Twenty years in regulated technology. Six reasons it shows up in the work.
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The auditor's number, not the demo number
Accuracy reported on a domain eval set, after human-in-the-loop verification. The figure that survives a regulator's review — agreed in writing during Discover.
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Lineage by default
Every prompt, retrieval, tool call and validation step written to an audit pack. The pack is the deliverable, not a slide we produce on request.
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Independently appraised
CMMI Level 3, ISO 9001, ISO 27001 and ISO 27701 certified. Approved supplier to The National Archives (UK). The credentials are externally verifiable.
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Actigen 2.0 framework
The Planner / Router / Executor / Validator loop has been hardened across more than four thousand projects. Every Generative-AI engagement runs through it.
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Engineers who stay with the work
Programmes here run for a decade. The engineer who designs the eval set is the engineer who signs the audit pack five releases later.
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99.1% on-time across 4,000+ projects
Delivery is governed, not optimistic. Every release ships with the runbook, the SLOs and the evidence pack the next reviewer will ask for by name.
Generative AI, independently verifiable.
Three engagements where the work has been measured, the lineage retained, and the regulator has seen the evidence pack. Filter by service or industry to widen the view.
Industry
135 years of lawmaking, queryable while the house is sitting.
A grounded RAG corpus over the entire paper-and-microfilm legislative history of the state — across multiple languages and scripts. Generative summarisation on top, with citation-by-default and reviewer override. The first legislative body, to our knowledge, to have been fully digitised.
Retrieval measured in days fell to retrieval measured in seconds. Programme since extended across several Indian state governments.
Read the case
80 million Norwegian records. Fifteenth to nineteenth century. Ninety-nine-point-five per cent.
No commercial model was trained on the handwriting. We engaged a Norwegian genealogist to label, fine-tuned a domain HWR model, and wrapped it in a custom indexing platform with reviewer-grade HITL. The accuracy threshold was cleared, not approached.
Delivered on schedule and above threshold. Every discrepancy remained traceable through the lineage layer.
Read the case
Invoice verification with an audit layer the CFO can place in front of the board.
Agentic invoice triage with the Actigen Planner / Router / Executor / Validator loop. Reconciliation against PO and shipment records. Human-in-the-loop review on exceptions. Every decision recorded with its reasoning trace.
Time −50%+ · Accuracy +50% · Throughput +30% · Payment accuracy +40%. Compliance held at 100% throughout.
Read the case
XHow you can work with us
Three engagement models. One operating standard.
The commercial structure adapts to your needs; our engineering governance remains absolute. Whichever model you select, you receive the identical audit pack, the same production SLOs, and the same dedicated accountability.
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Dedicated Teams
A fully integrated, multidisciplinary engineering pod—comprising machine learning experts, data engineers, and infrastructure ops—embedded directly within your organization for the duration of the roadmap.
- Best forMulti-quarter enterprise initiatives and complex products under continuous development.
- CommercialPredictable monthly run-rate; team capacity scales fluidly based on agreed notice periods.
- GovernanceAssigned delivery director; weekly steering committees; quarterly business reviews.
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Fixed-Scope Projects
A clearly specified deployment, delivered at a fixed cost, bound to a production timeline your compliance team can hold us to.
- Best forRapid pilots, architectural migrations, and proof-of-value implementations with defined accuracy thresholds.
- CommercialFixed-price engagement; milestone-based invoicing tied to explicit acceptance criteria.
- GovernanceStatements of Work (SoW) locked down with specific eval-set metrics and audit-pack parameters on day one.
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Staff Augmentation
Vetted, senior specialists integrated directly into your internal teams—operating seamlessly under your deployment workflow, while maintaining our strict technical execution standards.
- Best forAddressing immediate capacity deficits or injecting specialised expertise into your stack (RAG optimisation, LLMOps pipelines, model evaluation, HWR).
- CommercialTransparent per-resource monthly rate with a baseline three-month engagement commitment.
- GovernanceManaged via your internal development processes, backed by our structural compliance posture and engineering credentials.
XIHow we deliver
Four phases. Same rhythm every time.
Our execution cadence remains identical across every generative AI deployment. Data sample ingested. Evaluation metrics locked down. Iterative engineering through the loop. Production runtime with complete source traceability. The comprehensive audit pack is an automated byproduct of our architecture, never a manual document compiled after the fact.
01
Discover
Provide a representative sample of your infrastructure—a data corpus, a regulatory mandate, or an engineering backlog. Our technical team analyses it directly. A collaborative technical session, not a generic questionnaire.
02
Design
We define your explicit evaluation datasets, production accuracy targets, audit-pack specifications, and the exact model-routing and retrieval strategy—all committed to paper within five business days.
03
Build
Moving from architecture to production through the Actigen decision loop. Custom model refinement where required, integrated human-in-the-loop review interfaces, and rigorous evaluation gates enforced per release cycle.
04
Scale
Hardened production LLMOps. System cost and operational latency enforced through strict SLO boundaries. Compliance evidence packs generated on demand. Our historical 99.1% on-time deployment metric is managed and maintained here.
“The identical engineering loop powers every engagement. The only variable is which compliance gate matters most to your organization. ”
Contact us
Trusted by 100+ enterprise clients·20+ years engineering regulated technology·4,000+ complex production projects delivered·99.1% on-time delivery record
“What we asked for was a generative-AI system that could be put in front of a regulator. What we got was a system the regulator now asks for by name — with citations on every output and a lineage we can re-walk a year later.”Director of Information Systems Public-sector legislative client · Generative AI · RAG
XIITell us about your project
Send a sample. Receive a measured proposal.
Not a sales call. Not a qualification form. A representative sample, a clear question, and a measured pilot proposal returned within the working week.