Governed Agentic Operations

AI agents that hold up against real enterprise systems, regulators and operations. We’ve automated business processes since 1990. We use that experience to find where agents create value, build them into your workflows and run them with a full audit trail.

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The challenge

Models work. Getting them into production is the hard part.

Demos land and pilots run, but few reach production: in IDC research, only 4 of every 33 AI proofs of concept made it (CIO.com, March 2025). What holds them back sits underneath the model: strategy, engineering discipline and a way to turn a working model into a working system. All of that is solvable.

AI arrives as a tool rollout, not an operating model changeNew licences replace old ones while the workflows, and the results, stay the same.
Core systems weren’t built for agentsSecure, compliant integration with your core platforms has to come first, before an agent can do anything of consequence.
Model risk governance hasn’t caught upSR 11-7 in the US, OSFI Guideline E-23 in Canada, the EU AI Act and internal standards all expect explainability, traceability and a full audit trail, including for decisions a model makes on its own.
Delivery practice lags the technologyWithout specification discipline, evaluation gates and a governed context layer, every project starts over and relearns the last one’s lessons.
Teams haven’t been given the skillsPeople are asked to oversee agents without the roles, controls or metrics that make oversight work.

The opportunity

Turn back-office teams into automation-first operations.

Done properly, agentic AI reshapes the operating model instead of speeding up one process. Every pattern, standard and architectural decision you encode once is inherited by every project after it.

Agentic automation built into operations, not bolted onto the edges.
Faster processing on the workflows you target, measured against a baseline we set at the start.
Governance checks on every decision, including the ones an agent makes on its own.
Capacity moved from manual processing to AI oversight and validation.
Value tracked instead of assumed, with metrics built for AI productivity and adoption.

How we help

From workshop to production in four steps.

The people who design your solution are the same people who build and deploy it, so every recommendation is something we know can be built, governed and run.

Find the opportunitiesWorkshops and process mining map your workflows, bottlenecks and data, and set a baseline for current performance.
PrioritizeWe score each opportunity on business value, AI suitability, data readiness, integration effort and risk, and flag processes to redesign before any technology touches them.
Design the operating modelRoles, workflows, governance and compliance controls, with human oversight at the decisions that carry risk.
Build and embed the agentsWe co-team with your engineers to build against the roadmap, integrate with core systems and track the metrics that show it works.

What you get

A reference architecture for agents, and the means to reuse it.

It helps your teams decide quickly where agents create value, where conventional software is the better choice, and how to deploy agents with the right controls. Our approach turns agentic AI from one-off projects into an enterprise capability.

A prioritized pipeline that scores every candidate workflow on business value, AI suitability, data readiness, integration effort and risk, with an estimated benefit for each.
A reference architecture for orchestration, integration, identity, entitlements, retrieval and audit, decided once and reused everywhere.
A governance model that maps explainability, traceability, human-in-the-loop checkpoints and audit trails to SR 11-7, OSFI Guideline E-23, the EU AI Act and your internal model risk standards.
Working proofs of concept on real data and real systems, so the business case is tested before it’s funded.
A phased roadmap that sequences dependencies and expected benefits by phase.
A repeatable automation playbook, so your teams can keep scaling without us in the room.

Case study · A North American bank

Finding where AI agents pay off in day-to-day banking

A North American bank wanted to use AI agents to take friction out of its day-to-day banking operations. In 12 weeks we assessed its processes with 16 teams, proved the strongest ideas with working prototypes and built the roadmap the bank is now executing.

16 teams: Took part in a 12-week discovery
78: Manual processes identified and assessed
$10–15M: A year in identified savings from automation and cost avoidance, not yet realized

Read the case study

Questions

What buyers ask us.

How do you decide where to use AI agents?

We score each workflow on business value, AI suitability, data readiness, integration effort and risk before anything is built. Sometimes conventional software or platform modernization is the better answer, and we’ll say so.

Do AI agents replace our existing systems?

Not usually. Agents work across the systems you already have, orchestrating steps, reading context and moving work forward.

How long is the first phase?

The discovery in the case study on this page took 12 weeks with 16 teams. It ended with working proofs of concept and a prioritized roadmap the bank is now executing.

Which rules do you design for?

The model risk and AI rules that apply to you, such as SR 11-7 in the US, OSFI Guideline E-23 in Canada and the EU AI Act, plus your internal standards. We map controls to them and produce the evidence your reviewers ask for.

What does a production deployment need?

Clear use cases, reliable data, system integration, governance, monitoring, human oversight, testing and change management.

Our mind