Everything after the model is engineering.
Getting an AI system into production takes seven disciplines. We have all of them in-house, and we agree which ones your project needs before anything is built.
AI features built inside the products and systems your business already uses.
What we build
- Copilots, assistants and conversational interfaces
- AI features embedded in existing applications
- Multimodal interfaces across text, voice, image and document
- Connectors into CRM, ERP, ticketing and knowledge bases
- Feedback capture and product telemetry
Engineering depth
- Identity, permissions and tenant-aware data access
- Tool calling with validation, retries and fallbacks
- API design, observability and operational logging
- Escalation paths where the system should not act alone
- Front-end and back-end built by production engineers
Client value
- AI becomes workflow, not a side-channel chatbot
- Less context switching and manual lookup
- Differentiated capability inside existing products
- Measurable usage and quality feedback from day one
The question this answers
Why is my team copying answers out of a chat window by hand?
Enterprise AI quality is a data problem. Answers are only as good as what is parsed, indexed, governed and retrieved.
What we build
- Retrieval-augmented generation over private data
- Vector, keyword and hybrid retrieval pipelines
- OCR, layout parsing, form and table extraction
- Entity extraction, linking and knowledge graphs
- Ingestion for text, image, video and voice
- Cleaning, deduplication, enrichment and synthetic data
Engineering depth
- Chunking, metadata design and ranking strategy
- Permissions-aware indexing and source filtering
- Freshness, lineage and source attribution
- Evaluation sets for retrieval quality and grounding
- Pipelines that survive messy operational systems
Client value
- Institutional knowledge becomes findable and usable
- Grounded answers, with far less hallucination
- Better search across documents, cases and records
- The foundation everything else is built on
The question this answers
Why did it make things up about our own documents?
Classic machine learning still beats generative AI on prediction, ranking and decision support.
What we build
- Forecasting for demand, capacity, churn and risk
- Recommendation and personalisation engines
- Segmentation and propensity scoring
- Classification and anomaly detection
- Task-specific models tuned to one job
Engineering depth
- Feature engineering, label design, leakage prevention
- Backtesting, holdouts, calibration and explainability
- Batch, streaming and API deployment patterns
- Retraining triggers tied to real performance
- Metrics tied to decisions, not vanity scores
Client value
- Better planning, prioritisation and allocation
- Signal pulled out of operational and customer data
- Sharper targeting and personalisation
- Strong return without frontier-model inference costs
The question this answers
Do we need a language model for this at all?
We choose and adapt models against accuracy, privacy, cost and latency, then show the evidence behind the choice.
What we build
- Evaluation across frontier, managed and open-weight families
- Fine-tuning, LoRA adapters and domain adaptation
- Self-hosted open-weight and specialist domain models
- Private deployment in cloud, VPC, on-premise or air-gapped
- Hybrid patterns pairing frontier and local models
Engineering depth
- Task-specific benchmark suites and regression tests
- Training-data preparation, filtering and privacy controls
- Measured accuracy, cost and latency trade-offs per task
- Clear rules for when to use RAG, tuning or a private model
- Model choices kept portable as the market moves
Client value
- Higher domain accuracy and more consistent output
- Works for regulated and sovereignty-sensitive clients
- Sensitive data stays where policy requires
- Model choices you can re-benchmark as better ones arrive
The question this answers
Can we run this without our data leaving the building?
Tool-using AI can run multi-step work, within boundaries, with verification, and with someone accountable.
What we build
- Agentic AI design and implementation
- Multi-agent orchestration across tools and systems
- Workflow automation with plan, act and observe loops
- Human review, approval and exception handling
- Generative AI inside data and decision pipelines
Engineering depth
- Tool permissions, scoped credentials, action validation
- State, memory strategy and workflow recovery
- Policy for when an agent acts, asks or escalates
- Traceable execution logs for audit and debugging
- Built into real systems, not agent sandboxes
Client value
- Complex process work automated across systems
- People keep control where judgement matters
- Less manual routing, triage and repetitive effort
- Auditable automation fit for regulated work
The question this answers
What happens when it does something we did not approve?
Production AI needs the discipline of production software: testing, monitoring, change control and governance.
What we build
- LLMOps and MLOps deployment pipelines
- Version control for prompts, models, data and evaluations
- Monitoring for latency, cost, quality, drift and failure
- Guardrails, moderation, PII redaction, output validation
- Governance, auditability and bias testing
Engineering depth
- Offline and online evaluation suites with pass/fail gates
- Golden datasets, adversarial and regression tests
- Observability across prompts, retrieval, tools and output
- Safety layers for hallucination and policy enforcement
- Privacy, data residency and responsible AI by design
Client value
- Far less risk of surprise behaviour in production
- Measurable confidence for leadership before rollout
- Security and compliance expectations met
- Systems that stay maintainable after the build
The question this answers
It worked in the demo. Why is it worse now?
Architecture decides whether AI is fast enough, affordable enough, private enough and scalable enough to keep.
What we build
- Provider-agnostic architecture across platforms and APIs
- Model routing for quality, latency, cost and fallback
- Private GPU serving on optimised inference runtimes
- Cloud, hybrid, VPC, on-premise and air-gapped patterns
- Vector stores, caches, queues and async processing
Engineering depth
- Cost and latency profiling per model, task and workflow
- Quantisation, batching, caching and capacity planning
- Secure networking, secrets management, tenant isolation
- Data residency, sovereignty and no-egress architecture
- Rollout design that survives pilot to scale
Client value
- Inference spend stays predictable and under control
- Enterprise security and deployment constraints met
- Faster responses and more reliable systems
- Architecture separated from provider, with no lock-in
The question this answers
Why is the inference bill four times the estimate?
No unnecessary politics, demands or prima donnas here, just great developers, UX teams and people who are passionate about delivering amazing customer experiences for your business. I couldn’t recommend them highly enough!BBC Senior Manager
It starts with two days.
Enough to know what to build first, and what it will cost.