Service
Practical AI integrations — from internal copilots to customer-facing features — grounded in your data.
Overview
Most AI projects fail not because the models are wrong but because the problem was wrong. We've seen teams spend months fine-tuning a model to do something that a well-designed retrieval pipeline would do better, cheaper, and with fewer failure modes.
We help you find the right problem first — one where AI creates genuine, measurable value — and then build a production system robust enough to trust with real users. That means guardrails, fallback paths, evals, and monitoring from day one.
AI Implementation Process
Each phase produces concrete deliverables. Nothing moves to the next stage until you've reviewed and approved the outputs.
System Architecture
Retrieval-Augmented Generation separates your knowledge from the model's weights — making updates cheap and answers auditable.
Phase Breakdown
Week 1
We map every data source you have — structured, unstructured, and semi-structured — and assess quality, freshness, and accessibility. We also audit manual processes that are candidates for automation, scoring each on volume, error rate, and business impact.
Weeks 1–2
We score each potential use-case on two axes: business impact (revenue, cost, or risk reduction) and technical feasibility (data readiness, model availability, integration complexity). The highest-impact, lowest-friction use-case becomes the POC.
Weeks 2–3
A two-week prototype that tests the core assumption. We define success criteria before we start and measure against them rigorously. If the POC doesn't hit the target, we pivot before committing to a full build.
Weeks 4–6
Production-quality integration with your existing systems — APIs, databases, internal tools, and user interfaces. We implement prompt versioning, output validation, human-in-the-loop escalation paths, and usage tracking from the start.
Week 6
Deployed behind feature flags with a staged rollout — starting with internal users only. Guardrails active from day one: input/output filtering, confidence thresholds, and fallback to deterministic paths when the model is uncertain.
Ongoing
We track model accuracy, latency, token cost, user satisfaction, and edge-case failure rates. Regular evals run against a golden dataset that grows as the system encounters new inputs. We recommend model updates only when the data supports it.
Technology
We're tool-agnostic — we'll adopt your existing stack where it makes sense.
LLM Providers
Frameworks
Vector Stores
Backend
Data & ETL
Evals
FAQ
For most business use-cases, RAG is cheaper, faster to update, and easier to debug than fine-tuning. Fine-tuning is most valuable for style/tone consistency or specialised vocabulary. We'll recommend the right approach for your specific situation.
By designing systems that are hard to hallucinate in. Tight retrieval contexts, explicit uncertainty signals, citation tracking, confidence thresholds that trigger human escalation, and regression evals that catch problems before they reach users.
Yes. Modern LLM integrations require strong software engineering, not ML research. We bring the AI expertise; your engineers can operate and maintain the system after we build it.
We design for data minimisation — sending only what's necessary to external APIs. We document every data flow, implement PII detection and redaction where needed, and can recommend on-premise or private-cloud model deployments if your data cannot leave your infrastructure.
Typically: monthly model performance review, prompt tuning, eval updates, cost optimisation, and a defined number of engineering hours for feature additions. We stay involved so the system improves as your data grows.
Tell us about your project and we'll follow up within one business day to set up a discovery call.