# 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 systems & product integration

Copilots, assistants and AI features embedded in real products and systems of record.

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?

### Data, search & knowledge engineering

RAG, hybrid search, document AI, extraction and knowledge graphs over private data.

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?

### Predictive & analytical ML

Forecasting, recommendation, segmentation and task-specific models where classic ML wins.

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?

### Private & customised models

Fine-tuning, adapters, open-weight models and private or air-gapped deployment.

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?

### Agentic automation & human-in-the-loop

Tool-using agents, multi-agent workflows, approval gates and traceable automation.

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?

### AI operations, evaluation & trust

Monitoring, evaluations, guardrails, drift detection, governance and auditability.

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?

### AI infrastructure, cost & performance

Model routing, GPU serving, quantisation, hybrid cloud, VPC and no-egress architectures.

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.

- [Inside the AI Accelerator](https://www.uicdigital.com/get-started)

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