Services
Generative AI applications
Generative AI measured on uplift, not demos. LLM products, agents and internal copilots built on your data and embedded in your workflows, with evaluation and guardrails from day one. We are model-agnostic: the right model per problem, not the one we are paid to recommend.
The process we optimise
The process we optimise: how knowledge work reaches a decision
Generative AI earns its place inside one process above all others: the flow of knowledge work that ends in a decision or a customer-facing response. Someone reads, drafts, looks something up, checks it against policy, and acts. That process exists to turn scattered information into a good decision, quickly and consistently. When it runs slowly, the cost is not just time. It is inconsistent answers, decisions made on stale context, and skilled people spending their day on retrieval rather than judgement. We optimise that process, not the technology for its own sake.
Before and after
What changes when the system is rebuilt
The manual process today
- People draft from a blank page every time, re-solving the same problem in slightly different words.
- Answers depend on who happens to pick up the work and what they remember, so quality swings case by case.
- Finding the right context means opening five systems and trusting that the version in front of you is current.
- Triage and prioritisation sit in an inbox, and the backlog grows faster than anyone can read it.
The rebuilt system
- A copilot grounded in your own documents and data drafts the first pass, so people edit and decide rather than start cold.
- The same guardrails apply to every request, so the answer no longer depends on who is on shift.
- Retrieval is built into the workflow, citing the source it drew on so the human can check it in seconds.
- The queue is read, ranked and routed automatically, so attention lands on the cases that actually need judgement.
How we think about it
We start from the objective, then work outwards
The same discipline runs through every engagement: understand what the process is for, then design the system to serve it and measure against it.
Start from the objective
We begin with what the process is for, not with the model. Which decisions does this knowledge work exist to make, what does a good outcome look like, and where does the current flow leak time, consistency or accuracy. That objective becomes the measure everything is later held against.
Map the workflow
We follow the work as it actually runs, from the trigger through every read, draft, lookup and check to the point a decision is made. This shows the specific moments where a language model adds value and, just as important, the moments where it must not be trusted without a human.
Design the system around it
We build the application, agent or copilot around that workflow rather than bolting a chat box onto the side. It is grounded in your data, wired into the tools people already use, and shipped with evaluation and guardrails from day one so it fails safely and visibly.
Measure against the objective
We return to the objective we started with and measure against it: quality, consistency, time to a decision, adoption. Evaluation runs continuously, so drift shows up early and the system keeps earning its keep after go-live rather than degrading quietly.
An engagement, step by step
The engagement below is a representative shape, not an account of a specific client. It shows how a typical six to twelve week arc runs, from finding the real decision points to a measured production rollout. Every project is scoped individually.
- Weeks 1 to 2
Find the decision points
We sit with the team and trace the knowledge work end to end, isolating the handful of decisions where generative AI would change the economics. We agree the objective and the measures of success before anything is built.
- Weeks 2 to 4
Prototype against the real workflow
We build a working prototype on a slice of your own data and run it against real cases, not a sanitised demo. This proves the shape of the answer, surfaces the failure modes early, and tells us whether the value is there before the full build cost.
- Weeks 4 to 6
Stand up evaluation and guardrails
We put an evaluation harness around the prototype: test sets drawn from your cases, checks for accuracy and grounding, and guardrails for the answers the system must refuse or escalate. Confidence comes from the harness, not from a good demo day.
- Weeks 6 to 9
Build the production application
We build the real thing on your stack, integrated with the systems people already work in, model-agnostic so the right model serves each part of the problem. It is tested, version-controlled and designed for your team to own.
- Weeks 8 to 11
Roll out where the work happens
We put the system in front of the people who do the work, in the tools they already use, with the training to make it stick. Rollout is incremental, so adoption and edge cases are handled as they surface rather than in one risky switch-over.
- Weeks 10 to 12
Measure and hand over
We measure against the objective set in week one and hand over the runbooks, the evaluation suite and the documentation. Your team owns what we built, with monitoring in place so quality stays honest as usage grows.
The deliverable is a generative AI system your team owns and can defend, measured on the decision it was built to improve rather than on the demo that sold it.
What you get
- Use cases ranked by value, feasibility and risk
- A production LLM application, agent or copilot on your data
- Evaluation harness and guardrails from day one
- Usage and uplift measurement after go-live
How it typically runs
Typical engagement: 6 to 12 weeks, 2 engineers, outcome-linked pricing.
Diagnose and rank use cases
Weeks 1 to 3
Prototype
Weeks 3 to 6
Build and evaluate
Weeks 6 to 9
Productionise and measure
Weeks 9 to 12
Indicative timeline. Every project is scoped individually: book a discovery call and we will provide a detailed proposal within 48 hours.
Where it fits
This practice is delivered through both QuantSpark engines, depending on whether the answer is rollout or build.
Model-neutral by default
We evaluate Claude, GPT and open-weights models head to head for every problem, and recommend whichever wins on cost, latency and accuracy.
Proof
Generative AI applications in the field
Automating order entry from inbox to ERP
A private-equity-backed European manufacturer of engineered wood products
Predicting churn to protect a compliance SaaS business
A health and safety compliance SaaS and accreditation business, owned by a UK private equity house
Generative AI that turns days of government research into minutes
A UK government department