LLMs that earn their seat at the table.
Copilots, assistants, RAG search, and agents — engineered around your data, your guardrails, and the SLAs your business actually cares about.
How we approach generative ai solutions.
Most generative AI pilots impress in a demo and disappoint in production. The gap is rarely the model — it is everything around it: retrieval quality, evaluation harnesses, prompt versioning, observability, cost ceilings, and a clear story for when the model is wrong.
We build the whole system. The LLM is one component. The retrieval layer, the eval suite, the human-in-the-loop fallback, and the telemetry that proves it is working are all first-class — from the first sprint, not after the launch goes sideways.
Best fit for
- Teams with a measurable product, operational, or platform outcome.
- Leaders who want senior engineers accountable for delivery decisions.
- Systems where launch quality, security, and handover matter commercially.
Not a fit for
- Staffing-only requests where nobody owns outcomes or technical quality.
- Projects that need the cheapest possible build, regardless of maintainability.
- Big-bang programmes with no room for discovery, proof, or staged cutover.
What you get in week one
- A named technical lead and communication rhythm.
- Outcome map, risk register, and first-slice recommendation.
- Access plan, repository/cloud checklist, and demo schedule.
Concrete artefacts, not just engineering activity.
Every engagement leaves your team with working software and the operational assets needed to own it: architecture records, dashboards, runbooks, and handover notes.
Generative AI Solutions roadmap with outcome metrics and assumptions
Architecture decision records and integration contracts
Delivery dashboard covering scope, risks, burn, and demo outcomes
Production code, tests, CI/CD, and environment documentation
Security, accessibility, and performance checklist
Runbooks, handover notes, and operating model recommendations
Start small, build fixed-scope, embed a squad, or stay for support.
Discovery
One to two weeks to shape the outcome, risks, and plan.
Fixed-scope build
Milestone-led delivery for a well-defined product or platform slice.
Embedded squad
A senior cross-functional team working inside your cadence.
Ongoing support
Operations, optimisation, roadmap delivery, and handover support.
A typical path from first workshop to production.
Week 1
Discovery, access, and risk map
Align on the generative ai solutions outcome, validate constraints, and define the first demo-able slice.
Weeks 2–3
Architecture and first working slice
Stand up the delivery environment, agree technical decisions, and ship the first thin slice to staging.
Weeks 4–8
Build, measure, and de-risk
Weekly demos, production-shaped infrastructure, testing, observability, and stakeholder feedback loops.
Launch
Harden, cut over, and hand over
Security, performance, accessibility, go-live runbook, and a practical ownership handover.
Risk reduction is part of the scope.
We make risks visible early: security posture, data migration, accessibility, performance, operational handover, and ownership. The risk register is reviewed in demos alongside working software.
A short list, so the engagement starts with momentum.
You do not need a finished spec. You do need a few things in place so senior engineers can move quickly instead of waiting.
- A named decision-maker who can prioritise the generative ai solutions scope
- Access to the people who understand the current process and its edge cases
- Access to systems, data samples, and environments (read-only is fine to begin)
- The constraints that matter: compliance, deadlines, budget envelope, integrations
- A definition of success we can measure — even a rough one to sharpen together
The expensive failure modes we have seen before.
Most of the cost in this work comes from a handful of avoidable errors. We design the engagement to keep you out of them.
- Scoping the generative ai solutions too broadly before anything ships and learns
- Treating security, accessibility, and operability as launch-day work
- Building on assumptions that were never validated with real users or data
- No clear owner, so decisions stall and momentum quietly drains away
- Skipping the handover, leaving a system nobody on your team wants to touch
Indicative shapes, so you can budget before we talk.
Every project is scoped to its outcome, so these are guides, not quotes. They give you a realistic sense of duration, team shape, and where the value lands.
Discovery sprint
1–2 weeksValidate the outcome, map risks, and leave with a costed plan and a fixed first milestone.
Team: 1 senior engineer + part-time architect
Fixed-scope build
6–12 weeksA well-defined product or platform slice delivered to production against agreed milestones.
Team: 2–4 senior engineers + design as needed
Embedded squad
3+ monthsA cross-functional team working inside your cadence, owning delivery alongside your people.
Team: Lead, senior engineers, product/design
No exact budget required to start. A 30-minute scoping call turns these shapes into a firm plan and a fixed first milestone.
The problems this work exists to solve.
Before we talk solutions, we get specific about what is actually costing you time, money, or sleep. These are the patterns we see most often.
The AI demo never reaches production
A promising prototype stalls because no one can vouch for its accuracy, cost, or safety at scale. The gap between “impressive in a notebook” and “trustworthy in a product” is where most initiatives die.
Hallucinations erode user trust
Without retrieval grounding and evaluation, the model invents answers — and a single confident-but-wrong response can cost you the credibility you spent years building.
No way to measure if a change helped
Prompt and model tweaks are shipped on vibes. With no golden set or graders, every release is a guess and every regression is discovered by a customer.
What you can expect.
RAG that actually retrieves
Hybrid search, re-rankers, chunking strategies, and citation-grounded answers — tuned against an evaluation set, not a hunch.
Evaluation from week one
Golden sets, automated graders, and regression dashboards. We can show you whether the prompt change helped or hurt.
Guardrails and safety
Input/output validation, PII redaction, refusal handling, and prompt-injection defences appropriate to your risk profile.
Cost and latency controls
Model routing, caching, response streaming, and token budgets so the bill scales with value, not with traffic.
Model-agnostic by design
OpenAI, Anthropic, Google, open-weight models on your own infra — swappable behind a single internal interface.
Production observability
Per-request tracing, prompt versioning, feedback capture, and the dashboards your ops team needs to trust the system.
How we deliver.
Step
Discovery & scoping
One to two weeks. We confirm the outcome, the constraints, the risks, and the smallest first slice worth shipping.
Step
Architecture & plan
A short, opinionated document covers the system shape, delivery plan, named team, and the success metrics by week.
Step
Build in slices
Working software demoed every week. CI from day one. Staging environment from day one. No big-bang reveal at the end.
Step
Harden & launch
Performance, security, accessibility, and observability passes before go-live. Runbooks and handover that match.
Step
Operate & evolve
Stay on as long as it makes sense. Continuous improvement, capacity changes, and the next initiative when you’re ready.
The stack, give or take.
We pick per problem, not per pitch. These are the tools we reach for most often on this kind of work.
OpenAI
Anthropic
LangChain
LlamaIndex
pgvector
Pinecone
Weaviate
Python
TypeScript
AWS Bedrock
Where this work earns its keep.
The same engineering discipline, tuned to the regulation, scale, and accuracy demands of your sector.
Proof, in production.
We would rather show you a result than describe a capability. Here is a recent engagement where this work moved a number that mattered to the business.
Common questions.
- In almost every case we start with retrieval and prompting and only fine-tune when the evidence demands it. Fine-tuning is expensive, brittle to maintain, and rarely the bottleneck.
- Yes. We routinely deploy against private endpoints (Bedrock, Azure OpenAI, Vertex) or self-hosted open-weight models when contractual or regulatory constraints require it.
- Every project ships with a golden evaluation set and automated graders that run in CI. Prompt and model changes are accepted or rejected on data, not on vibes.
- We design for them. Citation-grounded answers, refusal patterns, confidence scoring, and human handoffs are part of the architecture — not patches we add after the first incident.
Ready when you are
Let’s talk about your generative ai solutions project.
Tell us what you are trying to ship. A senior engineer will follow up within one business day.
- Avg. engineer experience
- 9+ yrs
- Response time
- 1 day
- Code & IP ownership
- 100%