AI AGENTS &INTELLIGENCE
AI solutions for a smarter tomorrow.
Agents that read your systems, decide what to do and then actually do it — grounded in your own data, wired to your own tools, and built so you can see every step they took.
Ideas into intelligence
A chatbot answers. An agent acts. The difference is tools, memory and permission: the ability to look something up in your CRM, draft the reply, book the slot, and stop to ask a human when it is not sure.
We build the unglamorous half that makes that safe — retrieval over your real documents so answers are grounded and citable, evaluation sets so a prompt change cannot quietly regress, guardrails on what the agent may touch, and a trace of every call for when someone asks why it did that.
And we are honest about scope. If a rules engine solves it, we will say so. Agents earn their keep on judgement-shaped work: messy inputs, many tools, and an outcome worth checking.
Everything on the poster, in plain English
- 01
AI Agent Development
Goal-driven agents with tool access, memory and human checkpoints on the decisions that matter.
- 02
Agentic AI Systems
Multi-step, multi-agent workflows that plan, call tools, recover from failure and hand off cleanly.
- 03
Conversational AI
Support and sales assistants that hold context, know your catalogue, and escalate before they annoy anyone.
- 04
AI Voice Agents
Real-time speech agents for calls and in-product voice, with barge-in, low latency and call summaries.
- 05
RAG Systems
Retrieval over your documents, tickets and databases — chunked, indexed, evaluated and cited in the answer.
- 06
LLM Applications
Drafting, extraction, classification and summarisation built into the product, with cost and latency budgets.
Four steps, no mystery
- 01
Find the shaped work
We map where judgement is being spent by hand, and pick the task where an agent changes the day.
- 02
Ground it in your data
Sources connected, indexed and evaluated, so answers come from your material rather than the model’s guesswork.
- 03
Give it tools and limits
Actions, permissions, approvals and fallbacks — plus an eval suite that runs before every change ships.
- 04
Ship, watch, tune
Live with tracing, cost dashboards and a feedback loop, so quality is measured rather than assumed.
Deliverables
- Working agent in your environment
- Retrieval index over your sources
- Evaluation suite with scored runs
- Guardrails, approvals and audit trail
- Cost, latency and quality dashboard
- Runbook for operating and extending it
Tools & stack
- Claude
- OpenAI
- LangGraph
- MCP
- Pinecone
- pgvector
- Python
- TypeScript
- LangFuse
Chosen per project, not per habit — if something in your estate fits better, we use that instead.
Asked before you ask
Does our data go into training?
No. We use enterprise endpoints with training disabled, keep retrieval data in infrastructure you control, and can run open models on your own servers where policy requires it.
What stops it from getting things wrong?
Grounded retrieval with citations, a scored evaluation set that gates every release, hard limits on what it can act on, and a human approval step for anything irreversible.
Which model do you use?
Whichever wins on your evals for the cost and latency you need. The architecture keeps models swappable, so you are not locked to one vendor.
Can it work with our internal tools?
Yes — CRM, helpdesk, database, internal APIs, or anything with an interface. Tool integration is most of the actual work.
FROM CONCEPT TO IMPACT
Know the task, not the tech?
Describe the work you wish someone could hand off. We will tell you whether an agent is the right answer — and what it would take.