AI Implementation & LLM Integration
AI implementation means integrating large language models into your existing product so they solve a specific business problem. We build retrieval-augmented generation systems, document analysis and intelligent search on top of OpenAI, Claude and Gemini — scoped to a measurable outcome rather than a demo.
The hard part of adding AI is rarely calling the API. It is grounding the model in your own data so answers are accurate, controlling cost per request, handling the cases where the model is wrong, and knowing whether the feature actually improved anything.
We start from the business problem and work backwards. Sometimes the right answer is a retrieval system over your documents; sometimes it is a narrow classifier; occasionally it is that a rules-based approach would be cheaper, more predictable and entirely sufficient — and we will say so.
What's Included
Included in every AI Implementation engagement.
RAG system over your data
Retrieval-augmented generation grounded in your documents, so answers cite your actual content instead of being invented.
Document analysis pipeline
Automated extraction, summarisation and classification for contracts, invoices, support tickets or reports.
Intelligent search
Semantic search that understands meaning rather than exact keywords, with vector storage and ranking tuned to your corpus.
Prompt & evaluation harness
A test set that measures output quality, so prompt or model changes can be verified instead of eyeballed.
Cost & rate control
Caching, model routing and token budgeting to keep per-request cost predictable at production volume.
Guardrails & fallbacks
Input validation, output checks and graceful degradation for when the model fails or the provider is down.
How It Works
- 1
Identify the use case
We pick a problem with a measurable outcome — hours saved, response time cut, tickets deflected — and define how success is judged.
- 2
Prototype & evaluate
A working prototype against your real data, measured on an evaluation set so quality is a number rather than an impression.
- 3
Productionise
Caching, monitoring, error handling and cost controls added, then integrated into your product properly.
- 4
Measure & tune
Post-launch monitoring of quality and spend, with tuning as real usage reveals the cases you did not anticipate.
Technologies We Use
- OpenAI
- Claude
- Gemini
- LangChain
- pgvector
- Pinecone
- Node.js
- Python
Frequently Asked Questions
How do you stop an AI feature from giving wrong answers?
Mainly by grounding it. Retrieval-augmented generation restricts the model to your own documents and returns citations, so answers can be traced to a source. On top of that we add output validation, confidence thresholds, and an explicit fallback when the system does not have enough information — an honest 'I do not know' beats a confident fabrication.
Which model should I use — OpenAI, Claude or Gemini?
It depends on the workload. We usually benchmark two or three models against your actual data and compare quality, latency and cost per request rather than relying on general leaderboards. Systems are built so the model can be swapped later, because the pricing and capability landscape moves quickly.
How much does it cost to run an AI feature?
Inference cost depends on model choice, prompt size and volume. A support assistant handling a few thousand queries a month often runs in the tens of dollars, not thousands. We estimate this during prototyping and add caching and model routing so the bill scales predictably.
Can you add AI to our existing application?
Yes, and that is the common case. AI features are usually added as a service alongside your current stack, integrated through your existing API, with no rewrite of the surrounding application required.
Ready to start your project?
Tell us what you're building and you'll get a clear scope, timeline and fixed quote — at no cost.