AI & Machine Learning
Generative AI
Generative AI that earns its place — narrow, evaluated, and honest about what it cannot do.
Overview
Generative AI is the family of models that produce new content — text, code, images, audio — in response to a prompt. Large language models predict the next token in a sequence and, trained on enough text, become startlingly good at writing, summarising, answering questions and calling tools on your behalf. Diffusion models do the equivalent for images. The capability is genuinely new: for the first time, software can handle open-ended language and unstructured content without a human writing a rule for every case. That is why the interest is real and not only noise.
The gap between a demo and a product, though, is enormous, and it is where most generative-AI projects quietly fail. A model that dazzles in a five-minute demo will confidently invent facts, leak context it should not, cost more than anyone budgeted, and behave differently next month when the provider ships a new version. Value comes almost entirely from narrow, well-scoped applications with good data behind them and a way to measure whether the output is actually right — not from sprinkling “AI” across a product because a board asked for it.
We build generative-AI systems the way we build everything else: senior engineers, in production, honest about the trade-offs. In practice that means retrieval-augmented generation so answers are grounded in your documents rather than the model’s memory; agents and tool use where a task genuinely needs several steps; evaluation harnesses so we know quality before your users do; and guardrails plus human-in-the-loop wherever a wrong answer would cost something. We treat the model as one component in a real system, never as the system of record.
Best for — Organisations with a specific, language-heavy problem and the data to ground it, who want a scoped, evaluated generative-AI application built by engineers who will be honest about where the technology stops.
Why teams choose Generative AI
Handles unstructured language at scale
Tasks that used to need a person reading, understanding and reformatting text — summarising, classifying, extracting, drafting — become tractable in software without hand-coding a rule for every variation. That is the genuine step change, and it is worth pursuing where the work is real.
Grounded answers from your own knowledge
With retrieval-augmented generation, a model answers from your documents and cites where it found each claim, rather than reciting whatever it half-remembers from training. That is the difference between a useful internal assistant and a confident liar.
A faster path from idea to working assistant
Because so much capability comes pre-trained, a well-scoped copilot or support assistant can reach a usable state in weeks rather than the many months a bespoke model would have taken. The saving is real — provided the scope stays narrow and the evaluation is honest.
Why businesses choose Generative AI
- You want to know honestly whether generative AI actually fits your problem before you spend on it — including hearing that it does not.
- You need a system that grounds its answers and can be evaluated, not a clever demo that falls over the moment real users arrive.
- You care about data privacy, cost and compliance being designed in from the start rather than discovered in production.
- You want senior engineers who will run the thing, watch how it behaves with real inputs, and keep it working as the underlying models change.
What we build with Generative AI
The capabilities this technology is genuinely strong at — and what we most often build with it.
Retrieval-augmented generation (RAG)
We index your documents into a vector database, retrieve the passages relevant to each question, and give the model that context to answer from — with citations. This is how you turn a general model into an assistant that answers accurately about your business, and it is the pattern behind most genuinely useful text applications.
Agents and tool use
Where a task needs several steps — look something up, call an API, decide what to do next — we let the model call defined tools rather than guess. We are disciplined about it: agents add capability and failure modes in equal measure, so we use them only where a task genuinely needs multi-step reasoning, with tight scopes and clear stopping conditions.
Evaluation harnesses
Before a generative feature reaches users, we build a way to measure it: a test set of real inputs and expected behaviour, automated scoring, and regression checks so a prompt change or a model upgrade cannot silently degrade quality. Without evals you are shipping on vibes, which is how most of these projects come undone.
Guardrails and human-in-the-loop
We constrain what the system can say and do — input and output validation, refusal handling, grounding checks that catch answers with no source — and route anything risky or low-confidence to a person. The higher the stakes, the more the human stays in control rather than the model.
Structured extraction and summarisation
We turn messy, unstructured text — emails, contracts, tickets, PDFs — into clean, validated structured data, or into faithful summaries that a person can act on. This is often the highest-value, lowest-risk generative-AI work: bounded, checkable, and directly useful.
Model selection and fine-tuning
We choose deliberately between hosted frontier models and open models you host yourself, weighing capability, cost, latency, privacy and control. Fine-tuning has its place for consistent format and tone, but we reach for prompting and retrieval first, because most problems people try to fine-tune away are really data and context problems.
Use cases
Customer-support assistant
An assistant grounded in your help centre and past tickets that drafts replies or answers directly, cites its sources, and hands off to a human when it is unsure — reducing repetitive load without letting the model invent policy.
Internal knowledge search
A copilot over your documentation, wikis and shared drives that answers staff questions in plain language with links to the source, so people stop hunting through folders and out-of-date pages for something someone already wrote down.
Document processing and extraction
Pipelines that read invoices, contracts or forms and pull out validated structured fields, flagging anything low-confidence for review — turning slow, error-prone manual data entry into a fast, checkable workflow.
Content and code assistance
Tools that draft first-pass content, translate between formats, or help engineers scaffold and review code — always as an assistant to a person who edits and approves, never as an unattended publisher.
When Generative AI is the right choice
- You have a real, bounded problem involving language or unstructured content — answering questions over your own documents, drafting and summarising, extracting structured data from messy text, or assisting a person who stays in the loop. Narrow and well-defined is exactly where generative AI pays off.
- You have the data to ground it. RAG and useful copilots need a decent corpus — documentation, tickets, a knowledge base — that is reasonably clean and permitted for use. Good data is the single biggest predictor of whether a generative-AI project works.
- A human reviews or can override the output, or the cost of an occasional wrong answer is low. Generative models are probabilistic; they suit drafting, triage and assistance far better than final, unchecked decisions.
- Wrong for: anything deterministic that a rule, a lookup or a database query already solves. If the answer is knowable exactly, a model that is right most of the time is a downgrade, not an upgrade — and far more expensive to run.
- Wrong for: safety-critical or high-stakes decisions with no human in the loop — approving credit, diagnosing patients, anything legal or financial where a confident hallucination causes real harm. And wrong for use as a system of record: models generate plausible text, they do not store facts reliably, so the truth must always live in a real database.
Generative AI: pros and cons
Strengths
- Genuinely capable at open-ended language, code and content tasks that were impractical to automate before, without writing a rule for every case.
- Hosted models mean you can start building on top of strong capability immediately, without training anything, and validate the idea cheaply before committing.
- Retrieval and tool use let a general model work accurately over your specific, private, up-to-date data and systems.
- For assistive, human-in-the-loop workflows the productivity gains on drafting, search and triage are real and measurable when you scope them tightly.
Trade-offs
- Models hallucinate — they produce fluent, confident output that is simply wrong — and no amount of prompting removes this entirely. It must be designed around, never assumed away.
- Sending data to a hosted model raises real privacy, IP and compliance questions: where does the data go, is it retained or trained on, and are you allowed to send it at all?
- Running cost is easy to underestimate. Tokens, retrieval infrastructure and repeated calls in agent loops add up fast, and a design that ignores cost can be uneconomic at scale.
- The field moves extremely quickly. Models, prices and best practice change every few months, so anything built here needs maintenance and a deliberate strategy for change, not a fire-and-forget delivery.
Architecture
A serious generative-AI system is mostly the parts around the model. A typical build has an ingestion pipeline that chunks and embeds your documents into a vector store; a retrieval layer that finds the right context for each request; an orchestration layer — often built with something like LangChain or plain, well-structured code — that assembles prompts, calls the model, runs tool calls and enforces guardrails; and the application itself, usually behind a FastAPI service. The model provider sits behind an abstraction so we can switch or fall back without rewriting the product.
We keep the facts in a real database and treat the model as a stateless reasoning step over context we supply. That single decision prevents most of the trouble people attribute to generative AI: the source of truth is your data, retrieval decides what the model sees, and the model’s job is to reason over that context and produce output, not to remember your business. Every generation that matters carries its provenance — which documents, which tools, which version — so an answer can be traced and audited rather than merely trusted.
Performance
Performance here means latency, cost and quality together, because they trade off against each other constantly. Larger models are more capable but slower and dearer; retrieval adds a round trip; agent loops multiply calls. We measure real end-to-end latency and cost per request rather than guessing, then tune: caching repeated retrievals and responses, streaming tokens so the interface feels responsive while the answer is still generating, and choosing a smaller or cheaper model for the many requests that do not need a frontier one.
Quality is the performance dimension people forget until it bites them. A faster, cheaper pipeline is worthless if it answers wrong, so we hold quality with the evaluation harness while we optimise for speed and cost — never letting a latency win quietly become an accuracy loss. The honest position is that you are balancing three things at once, and the right balance is specific to how wrong an answer is allowed to be for your use case.
Security
The first security question with generative AI is where your data goes. We establish exactly what may be sent to a hosted provider, what their retention and training terms are, and whether a private deployment or a self-hosted open model is required for sensitive or regulated data — before a single record leaves your systems. Secrets and credentials never sit in prompts, and retrieval is scoped to what the requesting user is actually permitted to see, so an assistant cannot become a way to read documents someone should not.
Generative systems also bring their own attack surface. Prompt injection — malicious instructions hidden in retrieved content or user input — is a real and current risk, so we treat model output as untrusted, validate it, and never let it trigger a consequential action without checks. Tool use is constrained to a defined, least-privilege set. And because the output is probabilistic, we design on the assumption that it can be wrong or manipulated, keeping a human between the model and anything that matters.
Scalability
Scaling a generative-AI product is as much about economics as engineering. Hosted models handle the raw compute, so the questions become rate limits, cost per request at volume, and the throughput of your retrieval and vector infrastructure. We design for graceful behaviour under load — queuing, retries, fallbacks to an alternative model when a provider is slow or down — and we watch cost per request as closely as latency, because a design that is fine in a pilot can be ruinous at ten times the traffic.
The harder scaling problem is quality and change over time. As usage grows the inputs get stranger, the corpus grows, and the underlying models get replaced by their providers. We plan for that: the evaluation harness catches regressions when a model version changes, the retrieval layer is built to grow with the document set, and the provider sits behind an abstraction so a switch is a configuration decision, not a rebuild. Generative AI is not something you ship once — it is something you operate, and we build it to be operated.
Generative AI integrations & ecosystem
The technologies we most often pair with it — each links to how we work with it.
How we work
We start by pressure-testing whether generative AI is even the right tool. What is the actual problem, how would you know an answer was good, what data do you have, and would a simpler approach solve it more cheaply and reliably? If a rule or a search box does the job, we will tell you — and we would rather say so than sell you a model you do not need. If the fit is real, we scope it narrowly and define, up front, how we will measure success.
From there we build the evaluation harness early, often before the polished feature, because you cannot improve what you cannot measure and you cannot ship generative AI responsibly on impressions alone. We deliver a thin, real slice in production, watch how it behaves with genuine inputs, and iterate on retrieval, prompts and guardrails against the evals. The engineers who design the system run it, so the decisions are made by people who will live with the hallucinations, the costs and the 3 a.m. behaviour — which is exactly why they stay conservative and honest.
The service behind it
Delivered throughAI DevelopmentWhat we build with Generative AI
The disciplines this technology most often shows up in — from a first build to taking over and stabilising an existing one.
How we deliver
- 01
Discover
We map the system, the constraints and the business it serves — including the parts nobody documented.
Architecture brief
- 02
Architect
Decisions get made, written down and defended before a line of production code exists.
Decision records
- 03
Build
Short cycles against working software. You see progress in the product, not in a status deck.
Shipping increments
- 04
Operate
Monitoring, incident response and iteration. The system is alive, so the engagement is too.
Runbooks & SLOs
Industries we use Generative AI in
Domain knowledge changes what gets built. A few of the sectors we know before the first meeting.
Also in AI & Machine Learning
Why teams choose us for Generative AI
Honest about the hype
We separate what generative AI genuinely does well from what a board hopes it does. If your problem is deterministic, or the data is not there, or a simpler tool wins, we will say so before you spend — even when it costs us the project.
We evaluate before we ship
We do not put probabilistic systems in front of your users on impressions. Every generative feature comes with an evaluation harness, so quality is measured and regressions are caught before customers find them.
We operate what we build
We run the systems we ship, so we design for the reality of hallucinations, cost and model churn — guardrails, human oversight, provider abstractions — rather than the reality of a demo that never sees a hostile input.
Senior engineers only
A field this new and fast-moving punishes guesswork. The people designing your system have built and run generative-AI products before — there are no juniors experimenting on your project at your expense.
Typical timeline
- 01
Feasibility and scoping
One to two weeks establishing the real problem, the data available, how quality will be measured, and whether generative AI is genuinely the right tool — with a candid recommendation before anyone commits to a build.
- 02
Evaluation and prototype
Two to three weeks building the evaluation harness and a working prototype against real data, so we can measure quality honestly rather than judging by an impressive demo.
- 03
Production slice
A thin but real feature shipped to production with retrieval, guardrails and human-in-the-loop in place, tested against genuine inputs rather than a curated happy path.
- 04
Iteration and handover
Tuning retrieval, prompts and cost against the evals, hardening the guardrails, and handing over with the monitoring and evaluation set your team needs to operate it as the models change.
How pricing works
- A short, fixed-price discovery and feasibility engagement — is generative AI right for this problem, what would it cost to run, and what should the first version be — with a clear recommendation, including “don’t” when that is the honest answer.
- Fixed-scope builds for a well-defined application — a grounded assistant, an extraction pipeline, an internal copilot — quoted once the scope, data and evaluation criteria are agreed.
- Monthly senior engagement for ongoing generative-AI product work, where the scope evolves and you want continuity as models and requirements change.
- Running costs — model provider usage, vector infrastructure, hosting — are yours directly and estimated honestly up front, because they are ongoing and easy to underestimate.
Hire Generative AI engineers
Need Generative AI capacity on your own team? We embed named senior engineers into your existing team — reporting to your leads, working in your rituals — so you add capacity without a hiring cycle.
Hire Generative AI engineersCommon questions
Will the model make things up?
Yes — generative models hallucinate, producing fluent, confident output that is sometimes wrong, and no prompt removes this entirely. We design around it rather than pretend it away: grounding answers in your documents with retrieval, citing sources, checking that an answer is actually supported, and keeping a human in the loop wherever a wrong answer would cost something. What we never do is treat the model as a source of truth.
Do we need to fine-tune a model, or is prompting enough?
Usually prompting and retrieval are enough, and we reach for them first. Fine-tuning helps with consistent format, tone or a narrow, repeated task, but most problems people try to fine-tune away are really about giving the model the right context and data — which retrieval solves more cheaply and with far less to maintain. We recommend fine-tuning only when we can show it earns its keep.
Is our data safe if we use a hosted model like OpenAI or Claude?
It depends on the provider, the plan and how you configure it, which is exactly why we settle it before any data moves. We check retention and training terms, scope what may be sent, and, where data is sensitive or regulated, use private deployments or a self-hosted open model instead. Data privacy is a design decision we make up front, not something we discover in production.
How much does it cost to run?
More than most people expect, and worth estimating honestly before you build. Cost comes from model usage per request, vector and retrieval infrastructure, and — with agents — repeated calls that multiply quickly. We design to control it: caching, smaller models where they suffice, and tight agent scopes. We give you a realistic running-cost estimate early, because a design that ignores cost can be uneconomic at scale.
How do we know if generative AI is even right for our problem?
That is the first thing we test, and often the most valuable. If the answer is knowable exactly, a rule or a database query beats a model that is right most of the time. If there is no clean data to ground it, or no way to tell a good answer from a bad one, the project will struggle. Generative AI fits narrow, language-heavy problems with decent data and a tolerance for review. We will tell you honestly which side of that line you are on.
Building on Generative AI?
A technical conversation with the engineers who would do the work. If we are not the right fit, we will say so on the call.