LLM applications and assistants
Assistants and search over your own documents and data, grounded with retrieval (RAG) and citing their sources, so an answer can be checked rather than trusted.
AI development services
AI that works on your real data, not just in the demo.
AI software development for businesses that need it to hold up in production. We design and build AI applications, agents and AI features that stay accurate on real inputs, inside their permissions and inside budget, and can show why they gave an answer. Our engineers use AI to build them too, which is why delivery is faster than you might expect.
Every answer grounded in your sources, bounded by a guardrail, and measured against an evaluation set.
7+Years delivering
800+Projects delivered
$2MSaved a year by AI fraud detection we built
100%BotUp runs metered and logged
The real problem
Most AI projects stall between the demo and production.
- 01 The prototype worked on curated examples and fell over on real inputs.
- 02 Nobody could say whether a change made the system better or worse, because there was no evaluation set.
- 03 Costs drifted, because nothing capped what a single request could spend.
- 04 Legal asked what data the model touched, and the answer was a shrug.
Is AI the right answer?
When AI is the right tool, and when it is not.
AI earns its cost when the work involves judgement over messy information, repeated at volume. It wastes money when a rule would do. We will tell you which one you have before you spend anything on a build.
AI is likely the right tool
- Your team reads, sorts or extracts information from documents, emails or tickets all day.
- People need answers from your own knowledge, policies or product data, with the source attached.
- A decision repeats thousands of times and a model can score it: fraud, risk, routing, prioritisation.
- Your product would be more useful if it could predict, recommend or summarise for each user.
- A task spans several systems and could be run by an agent with tightly scoped permissions.
A conventional system may serve you better
- The logic can be written down as rules and rarely changes. A conventional system is cheaper and more predictable.
- The data the system would need does not exist yet, or you cannot get access to it.
- Nobody can say what a correct answer looks like, so there is nothing to test the system against.
- A wrong answer is costly and there is no person in the loop to catch it.
What we build
AI applications, agents and the engineering that keeps them dependable.
AI agents with guardrails
Agents that carry out tasks in your systems, limited to declared permissions, capped on spend, and stopped rather than allowed to drift.
AI features inside your product
Prediction, recommendation and intelligent search built into the software your customers already use, with per-customer cost limits and metering.
Document processing and automation
Pipelines that turn invoices, forms and contracts into structured data with confidence scores, and route the uncertain cases to a person.
Evaluation and monitoring
A test suite for model behaviour before launch, and monitoring for drift, latency and cost after it. Every change is measured before it reaches a user.
Integration with your systems
AI connected to the CRM, ERP and databases you already run, and kept model-agnostic, so a vendor’s price change is a configuration change rather than a rewrite.
Brilliant Systems helped us enhance our SaaS offering by embedding an AI engine capable of intelligent user behavior prediction and content recommendation.
Read the full review
How we ship it faster
Five times faster, at a fifth of the cost.
We use AI across the whole lifecycle: discovery and requirements, architecture, development, testing, documentation, debugging and maintenance. On an AI project the slowest part is rarely the model. It is producing the evaluation set, the edge cases and the test coverage that prove the system works. We generate the first draft of all three with AI tooling, and engineers review every line before it counts. AI accelerates the work. Our engineers stay accountable for the decisions and the software that ships.
- Evaluation setsFirst-draft test cases generated from your real documents, then reviewed and corrected by an engineer before they become the baseline.
- Adversarial edge casesSynthetic inputs written specifically to break the retrieval layer, so we find the failure before your users do.
- Scaffolding and testsBoilerplate, integration tests and fixtures generated and reviewed, leaving engineers on the parts that need judgement.
- Living documentationWritten alongside the code and regenerated when it changes, so handover is not a fortnight of archaeology.
An indicative comparison, not a quote or a guarantee. The gain depends on your data, your integrations and your approval process. We estimate every project individually and put scope, timeline and price in writing, with a like-for-like comparison against conventional delivery, before you commit.
Where it pays off
Five places AI usually earns its cost.
| Business function | What we build | What it changes |
|---|---|---|
| Customer support | Retrieval assistant over your own help content and ticket history | Deflects repetitive tickets; every answer cites its source |
| Sales operations | Call and transcript analysis with structured debriefs | Pipeline notes stop depending on who remembered to write them |
| Finance & back office | Document extraction with confidence scoring and human review queues | Manual keying falls away; exceptions get routed, not missed |
| Compliance | Policy checking with a full audit trail per decision | An assessor can see what was decided and on what basis |
| Product | In-app AI features with per-tenant cost caps and metering | Features ship without an unbounded cloud bill attached |
How we deliver
Three phases, and the first may conclude you should not build it.
01
Assessment
Typically 1–2 weeks
We look at whether AI is the right answer at all. Data readiness, a shortlist of use cases ranked by value against effort, and an honest view of where a conventional system would be cheaper and better.
- Use-case shortlist with expected value
- Data readiness assessment
- Build-or-do-not-build recommendation
02
Build and evaluate
Typically 4–10 weeks
The system and its evaluation set are built together. Nothing ships on the strength of a good demo. It ships when it beats the baseline on a set we agreed in advance.
- Working system in your environment
- Evaluation suite and baseline scores
- Cost model per request and per tenant
03
Operate
Ongoing
Deployment with monitoring on the things that actually break: drift, latency, spend and refusal rates. Plus the runbook for whoever is on call.
- Monitoring and alerting
- Rollback and versioning
- Runbook and handover documentation
What we work with
- Retrieval-augmented generation
- Vector and hybrid search
- Agent orchestration
- TensorFlow and PyTorch
- Evaluation and regression suites
- Guardrails and permissioning
- Prompt and model versioning
- Token and cost metering
- Human-in-the-loop review
- Fine-tuning where it pays
- Self-hosted and API models
- Observability and tracing
- Audit logging
Proof
We run AI in production ourselves.
BotUp is our own marketplace of AI agents, now in a controlled beta. Every run is scoped to declared permissions, capped before it can overspend, metered, and logged. Our engineers are on call for it, so the guardrails described on this page are ones we depend on ourselves. For a client, we built AI-driven fraud detection that saved a start-up $2M a year.
Questions we get asked
The questions that come up on almost every call.
What does an AI development company actually build for you?
Usually one of three things: an assistant or search tool over your own information, an agent that carries out a defined task in your systems, or an AI feature inside a product you already sell. In each case the model is the smaller part. Most of the work is the data access, permissions, evaluation and monitoring that make it dependable.
Which models do you use?
Whichever fits the job, and we design so that answer can change. Frontier APIs where quality matters most, open-weight or self-hosted models where cost, latency or data residency dominate. Committing a system to one vendor is how you end up rewriting it when their pricing changes.
How do you stop an AI system making things up?
Three things together: grounding answers in retrieved sources with citations, an evaluation suite that measures accuracy against a held-out set on every change, and letting the system say it does not know. A system that cannot decline is a system that will invent.
What does it cost to run once it is live?
We model this before you commit, per request and per tenant, and build hard caps so it cannot drift. Runaway inference cost is the most common unpleasant surprise in AI projects and it is entirely avoidable.
Can you work with our data without it leaving our infrastructure?
Yes. We can build on self-hosted models and private deployments when data residency is a hard requirement, as it often is in healthcare and finance. That choice is made in the assessment, before any code is written.
How long before we see something working?
The assessment takes one to two weeks, and a working prototype against your real data typically follows within weeks rather than months. We commit to a timeline in writing once we have seen the data, because data quality and access decide it more than the model does. Sometimes the assessment concludes that you do not need AI for this, which is the cheapest outcome of all.
Do you use AI to build the software as well?
Yes, and it is the main reason our delivery is faster. AI drafts tests, scaffolding, documentation and a first-pass code review. A named engineer reviews and approves every change, and nothing reaches production without that approval.
Can you add AI to a product or system we already have?
Yes, and it is often the best place to start. We connect AI to the data and systems you already run, behind the same permissions, and measure it against a baseline before it reaches users. One client asked us to embed prediction and recommendation into the SaaS product they already sold.
Related services
- Custom Software Development
- SaaS Development
- Enterprise Software Development
- Backend Development
- Cybersecurity
- How we work
Related case studies
Next step
Tell us what you want AI to do for your business.
A senior engineer reads every enquiry and replies within one business day with an honest view, including when a conventional system would serve you better than AI.
