Premium answers
AI that is accurate, useful and on-brand, tested before launch.
Service 05 of 15
AI & LLM development
AI agents, smart search and LLM features built into your product.
- Typical timeline
- 6–12 weeks
- Pricing
- Fixed quote
- Team
- AI engineer + product engineer
Our 3 promises
Your AI. Our goal.
Three promises we make on every AI project.
Ahead of the curve
The leading models, chosen for your quality, cost and privacy.
Safe by design
Your data stays yours, with guardrails and monitoring.
Tech stack
The stack behind your ai & llm development
Proven, well-supported tools at every layer. Picked for your product, never for our habits.
- ModelsLlama
- ModelsMost requestedOpenAI
- FrameworksLlamaIndex
- ModelsMost requestedAnthropic
- FrameworksFastAPI
- RetrievalPinecone
- FrameworksMost requestedLangChain
- RetrievalWeaviate
- RetrievalMost requestedpgvector
- CloudAWS Bedrock
- QualityGuardrails
- CloudMost requestedAzure AI
- QualityLangSmith
- QualityMost requestedEvals
What’s included
What you get
Everything in scope, from first workshop to launch.
01StartUse-case workshop
Find where AI saves the most time or money.
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The use-case workshop finds the tasks in your business where AI will save the most time or money, and checks whether your data is ready for them. It usually takes 1–2 weeks. You get a ranked list of use cases, an honest view of what is realistic and a fixed, itemised quote.
What you get
- Ranked list of AI use cases with expected benefit
- Review of the data each use case needs
- Recommendation on models and hosting, including UK or EU options
- Risks and limits for each use case
- Fixed, itemised quote for a proof of concept or full build
How it works
- Workshop with your team on repetitive or slow tasks
- Review sample documents and data
- Score each use case on value and feasibility
- Present the shortlist and fixed quote for approval
Good to know: Bring real examples, such as anonymised emails or documents, as they show quickly what AI can and cannot handle.
02BuildData & retrieval
Connect your documents and data safely (RAG).
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Data and retrieval connects an AI model to your own documents and data so its answers are based on your information, not guesswork. This approach is called retrieval-augmented generation (RAG). Access controls mean each user only gets answers from content they are allowed to see.
What you get
- Connectors to your document stores, databases or tools
- Vector search index using pgvector, Pinecone or similar
- Access control that follows your existing permissions
- Source links shown with each answer
- Automatic updates when documents change
- Data handling set up under UK and EU GDPR
How it works
- Agree which sources to include and who sees what
- Clean, split and index the content
- Test search quality with real questions
- Tune results until answers are accurate and sourced
Good to know: Out-of-date or duplicate documents lead to wrong answers, so a short clean-up of your sources pays off.
03BuildAI features
Assistants, agents, summaries and automations.
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AI features are the parts your users actually see: chat assistants, agents that complete tasks, document processing and automatic summaries. We usually start with a proof of concept in 2–4 weeks, then build the production feature in two-week sprints. Models come from providers such as OpenAI, Anthropic or AWS Bedrock, chosen for the task.
What you get
- Proof of concept tested on your real data
- Chat assistant or agent built into your product or tools
- Document AI that extracts, classifies or summarises content
- Clear hand-off to a person when AI is unsure
- Model choice explained, with cost per request estimated
- Source code and prompts in a repository you own
How it works
- Build a proof of concept on a narrow task
- Review results with you and agree the production scope
- Build the full feature with a demo every sprint
- Roll out to a small group before everyone
Good to know: Start with one well-defined task, as a narrow assistant that works well beats a broad one that often gets things wrong.
04LaunchSafety & testing
Evaluation, guardrails and human review.
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Safety and testing checks that your AI feature gives accurate, appropriate answers before real users rely on it. We build a test set of real questions, score the results, and add guardrails and human review where mistakes would matter. These tests run again every time a prompt or model changes.
What you get
- Evaluation set of real questions with expected answers
- Accuracy scores reported before each release
- Guardrails for off-topic, harmful or private content
- Human review step for high-risk outputs
- Logging of inputs and outputs, with GDPR-aware retention
- Test report you can share with your team or auditors
How it works
- Collect real examples and agree what a good answer is
- Run evaluations and measure accuracy
- Add guardrails and review steps where needed
- Repeat tests before every release
Good to know: Your subject experts need to spend a few hours marking example answers, as that sets the standard the AI is tested against.
05GrowMonitoring
Track quality, cost and usage after launch.
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Monitoring tracks the quality, cost and usage of your AI feature after launch, so you know it keeps working as models and data change. We set up dashboards and alerts using tools such as LangSmith. You get a regular review with clear actions, usually monthly.
What you get
- Dashboard of usage, response times and cost
- Spending limits and alerts for unexpected cost rises
- Ongoing quality checks against your evaluation set
- User feedback capture on answers, such as thumbs up or down
- Monthly report with suggested improvements
How it works
- Set up logging, dashboards and cost alerts at launch
- Review quality and feedback on a regular schedule
- Test new or cheaper models when they appear
- Apply improvements after checks pass
Good to know: Model providers update and retire models regularly, so budget for small changes over time rather than treating AI as finished.
How it works
From idea to launch
Every service runs through the same five steps. Here is what each one means for AI & LLM development.
01of 05
UnderstandWeeks 1–2Once
We find the tasks where AI saves real time, and check your data.
You getUse-case brief
What we do
- Workshop on tasks and pain points
- Review your data, documents and privacy needs
- Pick the use case with the best return
You get
- Use-case brief
- Data readiness review
- Success measures
From you
- 2–3 hours for workshops
- Sample data and documents
Tools
- Miro
- Notion
02of 05
PlanBefore any codeOnce
Model choice, architecture and a fixed quote you approve.
You getFixed quote & timeline
What we do
- Compare models on cost, speed and quality
- Design retrieval, prompts and guardrails
- Plan evaluation and rollout
You get
- Fixed, itemised quote
- Timeline with milestones
- Architecture and data flow
From you
- Review and approve the plan
Tools
- Figma
- Jira
03of 05
BuildTwo-week sprints↻ Repeats
Prototype first, then a production feature, with a demo every sprint.
You getWorking demo every sprint
What we do
- Prototype tested on your real data
- Integration into your app or tools
- Evaluation suite run on every change
You get
- A working demo every two weeks
- Accuracy and cost reports
- Access to your code repository
From you
- 30-minute demo every two weeks
- Feedback on sample answers
Tools
- OpenAI / Claude
- LangChain
- Python
04of 05
Test & launchEach release↻ Repeats
Accuracy, safety and cost checks, then a controlled rollout.
You getLive AI feature
What we do
- Accuracy and hallucination testing
- Safety, privacy and prompt-injection checks
- Staged rollout with monitoring
You get
- Live AI feature
- Evaluation and safety report
- Handover documents
From you
- User acceptance testing
- Go-live sign-off
Tools
- LangSmith
- AWS / Azure
05of 05
Support & growAfter launch↻ Repeats
Monitoring quality and cost, and improving as models change.
You getOngoing improvements
What we do
- Monitor answers, cost and speed
- Update models and prompts
- Monthly review of results
You get
- Monthly update
- Quality and cost reports
From you
- Monthly 30-minute review
Tools
- LangSmith
- Grafana
FAQ
AI & LLM development questions
What can an LLM do for my business?
Answer questions from your documents, draft emails and reports, summarise calls, classify tickets and pull data out of forms, with a human checking where accuracy matters.
What is RAG?
Retrieval-augmented generation: the AI searches your own documents first and answers from them, citing sources. It reduces made-up answers and keeps your data out of model training.
Is our data safe with AI models?
Yes, with the right setup. We use enterprise APIs that do not train on your data, UK or EU hosting where needed, and access controls and logs.
Which AI models do you use?
We choose per use case: OpenAI, Anthropic Claude, Google Gemini, or open-source models such as Llama hosted privately. We test and compare them on your real data.
How long does an AI project take?
A focused proof of concept takes 2–4 weeks. A production AI feature usually takes 6–12 weeks, delivered in two-week sprints.
How do you measure AI accuracy?
We build an evaluation set from real examples, track accuracy before launch and monitor answers in production, so quality is measured, not assumed.
Who owns the code?
You do. All IP is assigned to you in the contract, and the code, accounts and documentation are yours from the first sprint.
Let’s talk
Let’s talk
A 30-minute call with an engineer and a clear next step for your project.