One workflow at a time
A narrow agent that reliably owns one job beats a broad assistant that is impressive in a demo and unusable on Monday.
Agentic AI services
Agents built around
Agents built around
the work itself.
Six service lines covering the full path from a single automated workflow to a production agent platform. Every engagement starts with a real process, a measurable baseline and an honest answer about whether AI is the right tool.
01
Autonomous workflows
Custom AI Agents
We build agents around a specific job to be done: a defined goal, a bounded set of tools, explicit guardrails and an escalation path to a person. The agent reasons over your systems, takes the next best action and reports what it did.
Explore service 02 Revenue and serviceVirtual Sales & Support Agents
A virtual agent that knows your catalogue, pricing rules and policies, holds a natural conversation on the channel the customer already uses, and hands over to your team with full context when it matters.
Explore service 03 Grounded answersRAG & Knowledge Systems
We turn scattered documents, wikis, tickets and databases into a governed knowledge layer, then put a careful retrieval and answering pipeline on top with evaluation, permissions and source citations.
Explore service 04 Operations at scaleAgentic Process Automation
Beyond single-task bots: we map a real process, decide which steps are deterministic code and which need judgement, then orchestrate agents, queues and integrations so the whole process completes reliably.
Explore service 05 Conversational telephonyVoice AI Agents
Low-latency speech agents connected to your telephony and your systems, able to understand interruptions, confirm details accurately and transfer to a human with the conversation already summarised.
Explore service 06 Production foundationsAI Integration & Platform Engineering
Agents only create value when they are properly embedded. We build the integration layer, model routing, caching, prompt and eval infrastructure, observability and cost controls that keep an AI system dependable as usage grows.
Explore service
Our principles
AI should earn
AI should earn
its place in the system.
We start with the workflow and the risk, not the model. Data boundaries, evaluation, fallbacks and human oversight are part of the architecture — not an afterthought bolted on before launch.
Grounded and traceable
Answers come from your approved sources with citations. Where the agent is unsure, it escalates instead of guessing.
Humans stay in control
Clear escalation paths, approval gates on consequential actions, and a complete log of what the agent did and why.
Measured before launch
A fixed evaluation set and an agreed quality bar. We can tell you how often the agent is right before it reaches a customer.
Built into your stack
Agents connect to the CRM, ERP, helpdesk and database you already run — through APIs, webhooks or bridges where no API exists.
Owned after launch
Monitoring, tuning and cost control. An agent is a system to run, not a project to finish.
Engagement path
How a build
How a build
actually runs.
Four phases, with a real decision point after the prototype. If the evidence says an agent is not the answer, we will tell you.
Discovery
A short workshop on one workflow: volume, cost, failure modes and what success means numerically. Typically one week.
Prototype
A working agent on your real data, with the integrations stubbed where needed. Two to three weeks, and a genuine go/no-go.
Production build
Guardrails, evals, permissions, logging, live integrations and deployment into your environment. Four to eight weeks.
Run & expand
Monitoring, quality review, tuning and cost control on a retainer — then the next workflow once this one is trusted.
Questions
Before you commit.
What is an AI agent, in practical terms?
An agent is software that is given a goal, a set of tools it may use and rules it must follow. Rather than answering one question, it plans the steps, calls your systems, and either completes the work or escalates with context. The difference from a chatbot is that an agent takes action.
How long does a first agent take to build?
A working proof of concept on your real data typically takes one to three weeks. A production deployment with integrations, evaluation and monitoring is usually four to ten weeks depending on how many systems it must touch.
Will the agent invent answers?
We build retrieval-grounded systems that answer from your approved sources with citations, and we test them against a fixed question set before launch. Where the agent is not confident, it says so and escalates rather than guessing.
Where does our data go?
We design the data boundary with you first. Options range from provider APIs with no-training commitments to deployment inside your own cloud account. Retrieval is permission-aware, so the agent cannot surface what a user may not see.
Can agents work with our existing systems?
Yes. Agents connect through APIs, webhooks, databases or RPA where no API exists. We have integrated CRMs, ERPs, helpdesks, calendars, payment systems and bespoke legacy platforms.
What does it cost to run?
Running cost is driven by conversation or task volume and the models used. We route each task to the cheapest model that passes your quality bar, cache aggressively and give you a cost dashboard, so spend stays predictable.