AI Systems
AI that works in production, not just in demos.
WHAT WE DO.
AI demos are easy. Production AI is hard. The gap between a chatbot that impresses in a presentation and one that handles 500 real users a day without hallucinating, looping or breaking is an engineering problem — not a prompt engineering problem. We have built AI systems in production and we know where they fail. We design guardrails, observability and human handoff paths before we write the first agent.
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Requests auto-resolved
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Saved weekly per team
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Average response time
EVERYTHING IN THE ENGAGEMENT.
RAG pipelines
Retrieval-augmented generation over your documents, database or knowledge base. Answers grounded in your data, not hallucinated.
[ 01 ]AI agents with tool use
Agents that can query your CRM, send emails, update records and call APIs — with confidence thresholds and human escalation paths.
[ 02 ]Chatbot integration
Embedded chat widgets, Slack bots, WhatsApp integration — wherever your users already are.
[ 03 ]Evaluation & evals
Automated test suites for your AI outputs. We define what good looks like and measure it, so regressions are caught before they reach users.
[ 04 ]Guardrails & safety
Input/output filtering, confidence thresholds, topic restriction and audit logging — production safety from day one.
[ 05 ]Observability & tracing
Every prompt, response, tool call and escalation is logged as a structured trace. You can replay any conversation and see exactly what happened.
[ 06 ]THE PROCESS.
Use case definition
We define exactly what the AI is allowed to do, what it must refuse, and what the success metric is — before building anything.
Data & knowledge audit
We map the data sources the AI needs access to and design the retrieval strategy.
Prototype & eval baseline
A minimal prototype against a test suite. We measure accuracy before we start optimising.
Agent & tool integration
Tools, APIs and data sources connected with explicit permission models and error handling.
Guardrails & handoff paths
Human escalation, refusal handling and safety filtering built before production traffic.
Production & monitoring
Deployed with full tracing, escalation rate dashboards and a plan for retraining or prompt updates.
Which AI model do you use?
GPT-4o for most use cases. We evaluate the right model for each task — including local models when data privacy requires it.
How do you prevent hallucinations?
RAG grounds answers in real data, confidence thresholds block low-certainty responses, and evals catch regressions before they ship.
Can the AI access our internal data?
Yes — we connect AI systems to your CRM, database, documents or APIs with appropriate access controls and audit logging.
How long does an AI system take?
A production-ready AI chatbot or agent pipeline typically takes 4–8 weeks depending on the number of tools and data sources.
EXPLORE MORE.
HAVE AN IDEA? LET'S BUILD IT.
Tell us what you're trying to build, improve or automate.