01
Reach
Source connectors, pipelines, MCP gateways and agent adapters.
Everything you run today, data and applications alike, made accessible to agents with no rewrite. The legacy system stays as it is and gets an adapter.
AI Engineering takes a use case from idea into production. One team covering data, applications, context and governance, working on the platforms you already own.
Most enterprise pilots deliver exactly what they promised: a great demo that impresses the room.
Six months later, though, nobody can say whether the agent still works, who owns it when answers start drifting, or what it was even tested against - because it was built for the demo, not for production.
An AI Engineering Pod is a team of engineers. Data, applications, context, and governance in one group, sized to take a few agents into production and then scale from what worked.
We build our own agents the same way we build yours, using AI to do the engineering. What we learn doing that goes into your build.
The pod starts on a scoped first wave from the Assess Session, delivers agents into production, and hands each one over ready to run. Scope grows from results. A Small engagement tackles one workflow across a few systems and goes live in weeks. A Medium engagement takes on a full process spanning several systems, with approval steps built in, and goes live within a quarter. Nobody starts large; you earn scale by proving it works first.
Source connectors, pipelines, MCP gateways and agent adapters.
Everything you run today, data and applications alike, made accessible to agents with no rewrite. The legacy system stays as it is and gets an adapter.
Your data was built to fill dashboards. An agent needs live data, it has to respect who is allowed to see what, and it has to show where every answer came from.
This layer is how it gets there. Retrieval that understands permissions, records matched across systems, freshness and lineage.
This is where most programmes stall.
Agent templates, guardrails, approval gates and a person approving the risky steps.
The two layers beneath it decide whether the agent survives production.
Each layer can use a tool you already own, or one we bring. The agent frameworks, connectors, and stack are all industry standard and replaceable.
What compounds is how they are assembled and what happens after go-live.
The engagement produces a system your team can run. Every agent arrives with:
Ask an agent the same question twice, and you might get two different answers. This means a test built for one 'right answer' has nothing to check against. So, we build an evaluation harness specific to the use case, run regression tests against that non-deterministic output, include adversarial test cases, and agree on accuracy and safety thresholds before the build even starts.
Every agent must pass a named gate before it reaches production, signed off by a named person. Because the threshold was agreed at the start, the final decision is just a check against a number everyone already signed off on, which lets your risk function approve an agent based on evidence, not guesswork.
Hold this list against any competing proposal and ask what you're actually getting.
Every agent we build lands in Managed Operations, watched around the clock on our own platform.
18 months of stalled PoCs taken to a governed agent on SAP and Workday. Policy-as-code, hard human approval, full audit trail.
One shared context layer now powers five live agents — two small, three medium spanning invoice matching, vendor master data, month-end close, purchase-to-pay exceptions, and intercompany reconciliation. Each new agent built on what the last one left behind, so the work compounded instead of starting over.
Ten questions, four minutes. See where you stand before you spend on another tool or pilot.
Five days at a fixed fee, giving you a scorecard and a plan for the first wave that is yours to keep.
A pod takes the first wave into production on the platforms you already own.
The same team runs it, for the long term.
Most enterprises can start with the systems they already run - once those systems are made usable by agents - with one partner accountable for what happens after launch day.