GENERATIVE AI IN PRODUCTION,
not just in the demo.
Generative AI development that survives contact with real users — grounded in your data, evaluated before it ships, and integrated into the systems you already run. We build the smallest reliable version first, prove it against measurable evaluations, and hand you models and code you own outright.
It is a production system,
not a clever prompt.
Generative AI development is the work of turning a large language or diffusion model into something a business can actually depend on. That is far more than a well-worded prompt. A production generative AI system is four things working together: a foundation model chosen for the job, a retrieval layer that grounds it in your own data so it answers from fact rather than invention, an evaluation harness that measures whether the output is right, and the integration that wires it into the tools, data, and workflows your team already uses. Skip any one of those and you have a demo, not a product.
This is exactly why most generative AI projects stall. A prototype that dazzles in a controlled demo behaves very differently once real users, messy inputs, and edge cases arrive. The model confidently invents an answer that is not in your data. Costs spiral because nobody measured token usage under real load. There is no way to tell whether a change made the system better or worse, because there was never an evaluation set to measure against. The gap between an impressive demo and a reliable product is where budgets quietly disappear.
We work production-first. We ground the model in your data with retrieval before we reach for anything more expensive, we build evaluations from day one so every change is measured rather than guessed at, and we fine-tune only when prompting and retrieval genuinely fall short. Where a simpler, cheaper approach does the job better, we say so plainly. The result is a generative AI system your team can run, extend, and trust — not one that only behaves when the founder is driving the demo.
Six generative AI services,
one production standard.
Generative AI work spans strategy, custom applications, and the unglamorous engineering that keeps them reliable. We scope each service to the outcome it drives and hold all of them to the same bar: grounded, evaluated, and owned by you.
Generative AI consulting & strategy
We help you decide where generative AI genuinely earns its place and where it does not. We map your use cases, data readiness, and risk, then hand you a prioritised roadmap with honest effort, impact, and feasibility for each. You leave knowing what to build first, what it is worth, and what to leave alone.
Custom LLM & RAG application development
We build bespoke applications on top of large language models, grounded in your own content through retrieval-augmented generation. Your documents, knowledge base, and data become the source of truth so the system answers from fact and cites its sources, rather than generating confident fiction. Bespoke to your domain, not a thin wrapper around a public API.
Model fine-tuning & customisation
When prompting and retrieval have been tried and a real gap remains — a specialised tone, a narrow domain, a structured output the base model keeps missing — we fine-tune. We prepare the datasets, run the training, and measure the result against the base model so you can see the customisation was worth it. We do not fine-tune for its own sake.
AI copilot & assistant development
We build copilots and assistants that live inside your product or your team's tools — drafting, summarising, answering, and taking guided actions in context. They work from your data, keep a human in control of anything consequential, and escalate the genuine edge cases instead of guessing. Intelligence your users feel without having to leave what they were doing.
Generative AI integration & deployment (MLOps)
We wire generative AI into your existing systems and stand up the operational plumbing that keeps it alive — API integration, caching, fallback, cost controls, model versioning, monitoring, and guardrails. This is the LLMOps engineering that decides whether a feature survives real usage or falls over the first busy week.
Prompt engineering & evaluation
We treat prompts and evaluations as engineering, not guesswork. We design and version prompts, build evaluation sets from real examples, and measure accuracy, grounding, refusal behaviour, and cost so every change is provable. Without a way to measure, generative AI features quietly rot — so we build the measurement in from the start.
The jobs generative AI
actually does well.
Generative AI is a general capability, but the value is in specific jobs. These are the applications teams most often ask us to build — each one a concrete task with a measurable outcome, not a science project.
Content & document generation
Generating first drafts, marketing copy, reports, proposals, and structured documents from prompts and your own templates. The system produces the boilerplate and the starting point; your people review and refine instead of starting from a blank page. Consistent tone, faster turnaround, humans still in control of what ships.
Summarisation & extraction
Turning long documents, call transcripts, tickets, and scanned files into short summaries and structured data. We combine language models with NLP and OCR to read messy inputs — contracts, invoices, forms — and pull out exactly the fields you need, with humans reviewing the edge cases rather than every page.
Code generation & developer tools
Internal developer copilots, code generation against your own conventions, test scaffolding, and documentation assistants. Built around your codebase and standards so suggestions fit how your team actually works, with the developer always the one who accepts or rejects the output.
Semantic search & knowledge assistants
Search that understands meaning rather than keywords, and assistants that answer questions from your wikis, product docs, and support history with sources attached. People find the right answer in seconds instead of digging through folders — grounded in your content, so it says "I don't know" rather than inventing.
Image & media generation
Generating and varying images, creative assets, and media on brand and at volume — product visuals, campaign variants, and design starting points. Diffusion models tuned to your style and guardrailed for appropriate use, so the output is usable rather than generic stock.
Personalisation & recommendations
Generating personalised messaging, product recommendations, and tailored experiences from user context and behaviour. The system adapts what each user sees and reads, and improves with use when you set up the feedback loop — measured against real engagement, not assumed uplift.
Chosen by the job,
not by the brand.
The stack that decides whether a generative AI feature holds up in production. We pick the right model and tools for the problem in front of us — and swap them when something better lands.
Frontier models from OpenAI, Anthropic, and Google where the capability earns it; open weights on your own cloud where privacy, cost, or control matters more. Grounding and evaluation are not optional layers we add later — they are how we keep a generative system honest from the first prototype.
Different sectors,
the same grounded discipline.
The engineering discipline stays the same; the data, the risk, and the acceptable error rate change. These are the sectors where we most often put generative AI into production.
Grounded assistants for policy and product queries, document extraction for onboarding, and drafting support — all with citations and a human on anything consequential.
Summarising clinical notes, drafting documentation, and answering from approved sources, where refusal and auditability matter far more than fluency.
Product description generation at scale, semantic search that understands intent, and personalised messaging tuned to real engagement.
On-brand content and creative variants generated at volume, with humans reviewing before anything is published.
Contract summarisation, clause extraction, and grounded research assistants that cite the source document instead of inventing precedent.
Tutoring assistants, content generation, and grading support grounded in approved curriculum, with guardrails around accuracy.
Document-heavy intake, claims summarisation, and underwriting support — extraction and drafting with a reviewer in the loop.
Listing generation, document summarisation, and semantic search across large property and lease portfolios.
In-product copilots, semantic search, and AI features that become part of the product's edge — built to be evaluated and owned.
Grounded support assistants and reply drafting that answer from your knowledge base and escalate the genuine edge cases to a person.
Summarising maintenance logs and manuals, and assistants that surface the right procedure from dense technical documentation.
Drafting proposals and reports, summarising research, and extracting structure from documents so senior people spend time on clients.
From your data to a system
you can trust.
Generative AI projects fail differently from ordinary software — the risk is a system that looks impressive in a demo and falls over in production. Our process is built around exactly that risk, end to end.
Data gathering.
We identify and collect the data the system will draw on — documents, knowledge bases, historical examples, and the sources it needs to ground its answers. We are honest early about whether the data is there and usable, because a generative system is only as reliable as what it retrieves.
Data preparation.
We clean, chunk, and index that data for retrieval — building embeddings, structuring content, and removing the noise that would otherwise poison the output. This unglamorous step is where most of the eventual quality is won or lost.
Model selection & fine-tuning.
We choose the foundation model that fits the job, cost, and privacy needs, and start with prompting and retrieval. We fine-tune only if a measured gap remains after that — preparing datasets and training a customised model, then proving it beats the base.
Evaluation & validation.
We build evaluation sets from real examples and measure accuracy, grounding, refusal behaviour, and cost against them. This tells us whether the system is genuinely working and whether each change is an improvement — rather than trusting a good-looking demo.
Deployment & integration.
We wire the system into your product and workflows and harden it for production — caching, fallback, cost controls, versioning, monitoring, and guardrails. The engineering that decides whether the feature survives real, unpredictable usage.
Monitoring & continuous improvement.
Once live, we watch quality, cost, and drift, and feed real usage back into the evaluations and prompts. Generative AI features get sharper with use when you set them up to — or stale when you do not. We design for the former.
The honesty that keeps
a system trustworthy.
Most disappointing generative AI comes from the same few decisions made badly. Avoiding them is most of the job, so we build every engagement around exactly these principles.
A language model is not always the answer. Where a rule, a search index, or plain automation does the job more cheaply and reliably, we will tell you — even though it means a smaller project. Reaching for generative AI when it is not needed is how good budgets get wasted.
A model that invents a confident wrong answer is worse than one that says it does not know. We ground systems in your data with retrieval, design refusal into the behaviour, and measure hallucination rather than pretending the risk is not there.
For most use cases, a strong foundation model with good prompting and retrieval gets you most of the way, at a fraction of the cost and complexity. We fine-tune only when that has been tried and a specific, measured gap remains — never because it sounds impressive.
A generative system without evaluation is a demo that never becomes a product. We build evaluation sets early so every change is provable and quality is visible from the first prototype — not something we bolt on after the feature has already started drifting.
Where this connects
across TechCirkle.
Generative AI development pulls on several of our capabilities. These are the ones it most often leans on.
Questions we get
often.
Generative AI is strongest at the language-heavy work that eats your team's time — drafting documents, summarising long material, extracting data from messy files, answering questions from a large body of knowledge, and generating content or code at volume. The efficiency comes from letting the system produce the first version and the routine cases while your people review and handle the exceptions. The gains are real where the job is well defined and measurable; we help you find those jobs rather than sprinkle AI everywhere.
We build custom generative AI applications on top of strong foundation models, grounded in your own data through retrieval, and we fine-tune a customised model when there is a measured gap that prompting and retrieval cannot close. For most needs, a well-grounded application on a frontier or open model outperforms training a bespoke model from scratch, at a fraction of the cost — so we start there and only go further when the evidence says to.
It depends entirely on scope. A grounded assistant or a focused integration into an existing product can be a few weeks of work; a bespoke application with fine-tuning, evaluation, and full production hardening is a larger, multi-phase build. Because we start with the smallest useful version and prove it before investing further, you fund the next stage from the value of the last one rather than committing a big budget up front. After a discovery call you get a real number to budget around, broken down by phase.
Three things, together. Grounding: we retrieve from your own data so the model answers from fact and cites its sources rather than inventing. Refusal: we design the system to say "I don't know" instead of guessing when it lacks the information. Measurement: we build evaluation sets and track hallucination, grounding, and accuracy as numbers we can improve — plus a human in the loop for anything high-stakes. We do not pretend the risk is zero; we manage and measure it.
Yes — integration is a large part of what we do. We connect generative AI into your products, data sources, and workflows through APIs and the operational plumbing around them: caching, fallback, versioning, monitoring, and guardrails. The goal is a feature that lives inside the tools your team already uses, not a separate island they have to remember to visit.
We design for the sensitivity of your use case. For most B2B work, the major providers offer enterprise terms that exclude your data from training and support zero-retention. For more sensitive or regulated cases, we build on open-weight models hosted in your own cloud, so your data never leaves your environment. Access controls, audit logging, and guardrails are part of the build, not an afterthought.
Almost never at the start. For most use cases, prompting and retrieval against a strong foundation model get you most of the way there, far more cheaply and simply than fine-tuning. We recommend fine-tuning only after those have been tried and there is a specific, measured gap they cannot close — a specialised tone, a narrow domain, or a structured output the base model keeps missing.
You do. The application code, the prompts, the evaluation sets, any fine-tuned weights, and the surrounding infrastructure are yours. We do not retain ownership or lock you into us. Some clients keep us on for ongoing improvement because it is convenient, not because they have to — you own what we build either way.