Agents and automations that take real actions across your tools, grounded in your data, with guardrails and humans in the loop where it matters, engineered to survive real use, not just a slick demo.
A chatbot answers; an agent acts. It calls tools, updates records and moves a workflow forward, and that is exactly why a demo is not the finish line. The gap between an agent that impresses in a sandbox and one that survives real users, real data and real edge cases is where most AI projects quietly fail.
We build the engineering around the model: retrieval so answers are grounded and cited, tool use with permissions, guardrails, evaluation and monitoring, and a human in the loop wherever the stakes require one. The result is automation you can actually trust with your business.
Agents that take real actions across your systems, book, update, draft, route, with permissions and audit trails.
Retrieval over your documents so answers are grounded, cited and current, not confidently wrong.
The repetitive, rules-plus-judgement work automated end to end, with humans kept in the loop where it counts.
Evaluation suites, guardrails and fallbacks so the agent behaves under inputs you did not script.
Approval steps and escalation designed in where the stakes are high, so automation never runs blind.
Logging, monitoring and token-cost control so the agent stays reliable and affordable at scale.
The real task, the data available and whether an agent is the right tool, said honestly before you spend.
A working agent on your data and tools, measured against clear success criteria, not vibes.
Guardrails, evals, permissions, monitoring and human-in-the-loop steps engineered around the model.
Deployed into your workflow under your accounts, with cost and quality watched from day one.
The mistakes we have seen cost founders the most, so you can avoid them.
An agent that dazzles on demo data is not an agent that survives real users. The production gap, edge cases, bad input, permissions, is the real work.
Without evaluation and guardrails, an agent that works today fails silently tomorrow. We measure and fence it before it touches anything that matters.
The skill is knowing where judgement must stay human. We design approval and escalation in, so automation helps instead of causing expensive mistakes.
Token and infrastructure costs can quietly balloon. We design for cost control so the agent is affordable at scale, not just in a pilot.
A chatbot answers questions; an agent takes actions, calling tools, updating records and moving a workflow forward, with humans in the loop where the stakes require it. We build both and advise honestly on which your use case actually needs.
Repetitive work that mixes rules with judgement: triaging and routing requests, drafting and updating records, retrieving and summarising from your documents, and multi-step workflows across your tools. We start by finding where an agent genuinely helps versus where it does not.
Grounding answers in your data with retrieval and citations, evaluation suites that test behaviour, guardrails and fallbacks for unexpected input, permissions on what it can do, and human-in-the-loop approval where the stakes are high. That engineering is the actual product.
Yes. We connect agents to your systems through tools and APIs with scoped permissions and audit trails, and ground them in your documents through retrieval, so they act on your real, current information.
It depends on scope and data readiness, so we usually start with a small prototype on your real data to prove value before a full build. Pricing is fixed by milestone, and built with a senior offshore team it costs far less than US or UK rates.
We design for it: choosing the right model per task, caching and retrieval to cut token use, and monitoring cost in production. An agent that is brilliant but unaffordable is not a solution, so cost control is part of the build.
We are model-agnostic and pick per task, typically Claude or GPT-class models, with the right retrieval and tooling around them. Because we default to the most capable current models, we move as the frontier moves.
Yes. The code, prompts, data access and accounts are in your name from day one. For sensitive or regulated work we scope data handling explicitly before building.