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Read articleAI Agent Development & LLM Integration
AI agents, LLM integration and automation built for production: retrieval, assistants and multi-agent workflows connected to your real data and systems.
What's included
- AI agent developmentMulti-agent systems that plan, act and hand off work
- LLM integrationOpenAI, Claude, Gemini, LangChain, retrieval-augmented generation
- AI-powered chatbotsDocument Q&A and support agents
- Workflow automationRoutine processes run by AI, with checks built in
- Data extraction & analyticsPulling structured, usable data out of raw sources
- Custom AI model integrationFine-tuning, deployment and operations
We use AI agents before we sell them
Every project we ship is built by a team of AI agents: one plans, one drafts the architecture, two write the backend and the interface, one tests and one deploys a preview. A senior engineer reviews the result and is the only one who can approve a release. So when we build agents for you, we are building the kind of system we depend on every day, and we know where they fail.
They fail quietly. An agent that is right nine times out of ten will sound just as sure the tenth time. That is why every agent we build has a narrow job, can only touch what the logged-in user is allowed to touch, logs what it did, and hands anything that moves money or changes records to a person first.
On OpenInterview.me, a US hiring platform, the AI part is a LangChain and OpenAI assistant that turns a candidate's resume into a profile they can share. It sits inside a production app with Stripe billing and scheduling, which is where most AI features should live: inside software people already use, not in a separate chat window.
Add AI to the system you already have
Most companies don't need a new AI product. They need the ERP, CRM or SaaS they already run to do a little more of the routine work.
- Ask your own dataStaff ask questions in plain language and get answers from your records, with links to the source.
- Read documents into fieldsInvoices, resumes and reports turned into structured data, checked by a person before it is saved.
- Draft the routineFollow-up messages, reminders and summaries written by AI, sent after a person approves them.
- Agents that respect rolesAn agent can only see and do what the person using it is allowed to.
A prompt, RAG or fine-tuning?
Three ways to make a model useful on your data. Most projects need the first two, very few need the third.
| Approach | Use it when | Trade-off |
|---|---|---|
| A well-written prompt | The task is general: summarise, classify, draft | Cheapest and fastest, but knows nothing about your data |
| Retrieval (RAG) | Answers must come from your documents or database | Needs clean, up-to-date data and a good search index |
| Fine-tuning | You need a fixed style or format at high volume | Costs more, and must be redone when your data changes |
How we keep AI features safe
The rules every AI feature we ship follows.
- Narrow jobs. One agent, one task, clear inputs and outputs.
- Same permissions as the user. Access is checked on the server, never left to the model.
- A person before money moves. Payments, deletions and record changes need a human approval.
- Logged and testable. Every prompt and answer is logged, and a test set catches regressions when a model or prompt changes.
- Swappable models. OpenAI, Claude or Gemini behind one interface, so you are not locked to one provider.
What an AI build costs
A typical range from our cost estimator. Adding AI to a system you already have usually costs less.
| Build | Typical range | Timeline |
|---|---|---|
| AI Chatbot | $12,000 to $19,500 | 6 to 9 weeks |
Built and running
Common questions
Can you add AI to our existing system?
Yes. We integrate assistants, document processing and automation into the tools you already use, without replacing everything.
How do you keep AI features reliable?
Every AI feature ships with guardrails and logging, plus human review where it matters. A senior engineer checks the output before it reaches your users.
How much does AI agent development cost?
A standalone AI assistant is typically $12,000 to $19,500. Adding one AI feature to a system you already run is usually less, and the written plan gives the exact figure.
Which AI models do you use?
OpenAI, Anthropic Claude and Google Gemini, chosen per task and kept behind one interface so the model can be changed later without rewriting the feature.
Is our data sent to the AI provider?
Only what a task needs. We send the minimum, use the providers' business terms that exclude your data from training, and keep logs on your own servers.
Ready to scope your AI agents & automation project?
Answer three quick questions and we'll send a written plan covering scope, team, timeline and estimate.
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