01
Discovery and consulting
Working out what is worth building, what the data will support, and what it is likely to cost to run. The honest outcome is sometimes that the idea does not need a model.
- Home
- Generative AI Development
United Kingdom
Generative AI & AI app development
The hard part of an AI product is rarely the model. It is grounding it in your data so it stops inventing things, controlling what it costs per request, and deciding what happens when it is wrong. We do discovery first, then build, and we will tell you when a conventional feature would serve you better.
Thanks — that’s with us.
A senior engineer reads every enquiry and will get back to you shortly.
Want to take it further now?
Book a time that suits you, or send a message and get an answer sooner.
- Discovery before build, including the option of not building
- Grounded in your data: retrieval, evaluation and guardrails
- UK-registered company, Companies House 16718403
2020
Building since
1,000+
Clients served
UK
Registered company
4.6/5
Rated on Clutch
Quick answer
What building with generative AI actually involves
Calling a model is trivial. Everything that makes the result trustworthy is not. A working generative AI product is mostly retrieval, evaluation, guardrails and cost control, with the model itself as a component rather than the architecture.
Retrieval is usually the centre of gravity. If the answer must be grounded in your own documents, contracts, policies, tickets, product data, then the quality of the system is decided by how well the right passage is found before the model ever sees the question. Most disappointing AI products are retrieval failures being blamed on the model.
Evaluation is the part that gets skipped and should not be. Without a test set and a way to measure whether a change made answers better or worse, every prompt adjustment is a guess, and the product degrades quietly as it is tuned. We build the evaluation harness alongside the feature, because retrofitting it is much harder.
Fine-tuning is occasionally the right answer and more often is not. It is good for format and tone, poor for teaching facts, and it adds a maintenance burden. We will usually recommend retrieval first and tell you plainly when fine-tuning would actually earn its keep.
What we build
What we do with generative AI
Eight areas. Most engagements begin with discovery, then a narrow first build rather than a platform.
02
Custom LLM applications
Products built around a model rather than bolted onto one: the interface, the retrieval, the state, the permissions and the fallbacks when the model is unavailable or unsure.
03
Retrieval-augmented generation
Grounding answers in your own documents and data, with citations. This is where most of the quality comes from, and where most of the engineering time goes.
04
Fine-tuning, when it fits
Used for consistent format and tone rather than for teaching facts. We will tell you when retrieval would do the job better and cheaper, which is most of the time.
05
AI agents
Multi-step work with tool access, built with explicit limits on what the agent may touch and a human checkpoint where the action is irreversible.
06
Chatbots and assistants
Customer-facing and internal assistants grounded in your material, with a clear handover to a person. A bot that invents policy is worse than no bot.
07
AI features in existing products
Adding capability to software you already run and sell, without rewriting it. Usually the lowest-risk way to find out whether your users actually want the feature.
08
Security, privacy and evaluation
Prompt injection handling, permission boundaries, data retention decisions, and an evaluation harness so quality can be measured rather than asserted.
Why projects stall
The demo works. The product does not
Nearly every stalled AI project we are shown had a convincing prototype. The gap between that and something people can rely on is where the engineering lives.
What goes wrong
- Confident answers that are wrong, with no way to tell which
- Costs that scale unpredictably with usage
- No test set, so nobody can tell whether a change helped
- Retrieval that finds the wrong passage and blames the model
- A prototype nobody will sign off for production
What we build in
- Answers grounded in your data, with citations back to the source
- Cost per request understood, capped and monitored
- An evaluation set, so changes can be measured rather than guessed
- Retrieval tuned and tested as a component in its own right
- Guardrails, logging and a defined behaviour when the model is unsure
Not sure whether this needs AI at all?
A fair number of requests we get are better served by a conventional feature. Discovery is free, and we will tell you if that is the case before anyone commits a budget.
In practice
What people ask us to build
Six recurring requests. These describe our work rather than named clients.
Ask questions of your own documents
Contracts, policies, manuals or case files, answered with a citation back to the clause. The quality is decided almost entirely by retrieval, and the citation is what makes it usable in a regulated setting.
Answers a professional can check
Deflection without misinformation
An assistant grounded in your help material and ticket history, with firm limits on what it will assert and a fast handover. The limits are the feature, not a compromise.
Deflection you are comfortable defending
First drafts inside the workflow
Quotes, reports, responses and summaries produced where the work already happens, in your house style, for a person to approve rather than to rewrite.
A starting point rather than a blank page
Structure out of unstructured input
Turning documents, emails and free text into fields a system can use, with a confidence signal so uncertain cases go to a person instead of silently through.
Clean data from messy input
What the company already knows
Search over wikis, drives and tickets that returns an answer rather than ten links, scoped to what each user is permitted to see. Permissions are the hard part and get built first.
Knowledge that outlives the people who had it
AI inside software you already sell
Adding a capability to an existing product without a rewrite, released narrowly so you learn whether customers use it before committing to a platform.
A tested feature instead of a bet
How we work
Discovery, narrow build, evaluate, widen
A narrow build that works beats a platform that nearly does.
1. Discovery
What the product needs to do, what data exists, what accuracy is good enough and what happens when it is wrong. Free, and it sometimes ends with us advising against the build.
2. Narrow build
One use case, properly: retrieval, interface, guardrails and cost control. Narrow enough to be judged honestly rather than demoed persuasively.
3. Evaluate
An evaluation set built from real questions, so improvements can be measured. Without this you cannot tell a better prompt from a lucky one.
4. Widen or stop
Extend to the next use case, or stop, on evidence. A product that does one thing reliably is worth more than one that does five things unpredictably.
Clear scope · Regular demos · Full handover
Honest fit
When generative AI is the right tool
It is a genuinely powerful tool and a poor fit for a lot of the problems it is proposed for.
A good fit when…
- The input is language or documents rather than tidy fields
- You have data worth grounding answers in
- Being approximately right, with review, is useful
- You can define what a good answer looks like
- You want evaluation built in rather than retrofitted
Not the right fit when…
- The task needs an exact, auditable, deterministic answer every time
- The underlying data is not there and nobody will create it
- Nobody internally can say what correct looks like
- A conventional feature would do the same job more cheaply
Sectors
Who we build for
Where language and documents are the raw material.
Legal and professionalContracts, precedent and document reviewFinance and fintechPolicy, compliance and reporting languageHealthcareRecords, referrals and clinical adminPropertyTenancy, survey and transaction documentsLogisticsDocumentation and exception handlingRetail and ecommerceProduct content and customer messagingSaaSAI features inside existing productsEverything elseIf the input is language, this applies
FAQ
Generative AI development — FAQs
What UK buyers ask before starting an AI build.
Still have a question?
Ask us directly — a senior engineer will get back to you.
Ask about your projectShould we fine-tune a model or use retrieval?
Retrieval, in most cases. Fine-tuning is good at shaping format and tone and poor at teaching facts, and it leaves you with a model to maintain as your data changes. Retrieval keeps the knowledge in a source you can update and lets the answer cite where it came from. We will say when fine-tuning genuinely helps, but it is the exception.
How do we stop it making things up?
You reduce it rather than eliminate it. Ground answers in retrieved source material, require citations, constrain what the system will assert, and define a clear behaviour for when it is unsure, which should be saying so rather than guessing. Anyone promising zero hallucination is overselling.
What will it cost to run?
That depends on request volume, how much context each request carries and which model you use, and it is one of the first things we estimate, because it decides architecture. Products that are affordable at a thousand users and ruinous at a hundred thousand are a common and avoidable mistake.
Where does our data go, and is it used for training?
We will tell you exactly what is sent to a provider, what is retained and for how long, and which arrangements keep your data out of training. Where that is not acceptable, open models running in your own environment are a genuine option and we will cost both.
How does this sit with UK GDPR?
We build to support your obligations: data minimisation in what is sent, an agreed retention policy, an audit trail and documented processing. The duties stay with you as controller. We will not tell you a build makes you compliant, because no supplier can do that.
Can you add AI to our existing product?
Usually yes, and it is often the sensible starting point. Adding a narrow capability to software you already sell tells you whether customers want it, at a fraction of the cost of building a separate AI product and hoping.
Do we own the result?
Yes. Code in your repository, running on your infrastructure, with documentation. We do not build systems that only work while you keep paying us.
How long does a first build take?
A narrow, well-defined use case is typically several weeks rather than several months. Anything described as a platform takes longer and we would usually argue against starting there. The estimate follows discovery, in writing, with its assumptions stated.
What does it cost?
It depends on the size of the site and the scope of the work, so we do not publish a fixed price: the honest range is wide. The first conversation and the audit are free, and the quote follows them, in pounds, once we both know what the work actually is.
Who do we contract with?
SpiderHunts Technologies Ltd, registered at Companies House under number 16718403, with an office at 182-184 High Street N, Office 14607, London E6 2JA. You are invoiced in pounds. The engineering team works from Lahore, which we state openly rather than implying a room full of desks in London.
Start here
Tell us what you want to build
Describe the product or the feature and who would use it. A senior engineer will come back with questions, the risks worth knowing about early, and a realistic range in pounds. If we think it does not need a model, we will say so.
- You tell us what you needTwo minutes on the form, or a message on WhatsApp.
- A senior engineer reviews itAnd comes back with questions, a realistic range and an honest view on fit.
- Free 30-minute scoping callWe talk through scope, options and a realistic estimate — with no obligation.
Thanks — that’s with us.
A senior engineer reads every enquiry and will get back to you shortly.
Want to take it further now?
Book a time that suits you, or send a message and get an answer sooner.