Services

Enterprise AI services

Four stages, taken in sequence or entered where it makes sense for your organization. Each produces something you can act on, and each ends with a decision about whether to continue.

01
Assess
Map how the work runs today, quantify what it costs, and rank the workflows worth changing.
02
Prove
Build one bounded workflow to a production standard and measure it against the baseline.
03
Implement
Extend across a function, with the integrations, access controls, and logging the business requires.
04
Operate
Monitor quality and cost, handle exceptions, and keep the system current as the work changes.

Most organizations begin at Assess. Those with a clearly defined target workflow sometimes begin at Prove.

Stage 01

AI Opportunity Assessment

Move from scattered AI ideas to a prioritized, defensible investment plan.

Find and rank the workflows where AI can produce meaningful, achievable value — and identify the ones that are not worth attempting yet.

What this stage covers

Stakeholder interviews across the people who run, review, and depend on the workflow
Current-state workflow mapping, including the handoffs and approvals that rarely appear in process documentation
Friction, cost, and risk analysis against real volume
Data and systems-readiness review
Security and governance considerations, surfaced early rather than at procurement
Prioritized use-case portfolio with effort and value estimates
ROI model your finance team can interrogate
Recommended pilot and executive implementation roadmap

You end with a ranked portfolio, a defensible business case, and a specific recommendation for what to build first.

Stage 02

Production Pilot

Prove the operational and financial case before committing to broader transformation.

Take one bounded workflow and build it to a production standard — integrated, controlled, measured, and used by the people who do the work.

What this stage covers

Solution architecture, reviewed with your technology and security stakeholders
Working implementation built to production standards, not demonstration standards
Integration with approved systems and data sources
Human review steps and explicit exception handling
Evaluation framework that tests quality against cases you care about
Security and operational controls
Adoption plan for the team that will actually use it
Before-and-after performance measurement, and a recommendation on whether to scale

You end with a working system in real use, a measured result against a baseline, and enough evidence to decide whether to expand or stop.

Stage 03

Enterprise AI Implementation

Move from an isolated success to reliable, governed AI operations.

Deploy integrated AI capability across a department, a workflow family, or a business function, with the controls the organization requires to run it at scale.

What this stage covers

Multi-workflow implementation built on shared architecture rather than repeated one-offs
Systems and data integration across the estate the work depends on
Identity and access controls aligned to existing policy
Audit logging suitable for internal review
Evaluation and quality monitoring in production
Governance workflows — who approves changes, who reviews exceptions, who owns the system
Deployment infrastructure and release process
Training and adoption support, plus executive performance reporting

You end with AI capability that operations, security, and leadership can each defend within their own remit.

Stage 04

Managed AI Operations

Keep production AI effective as models, workflows, data, and organizational needs change.

Maintain, evaluate, improve, and extend the systems after launch — because an AI-enabled workflow is not a project that finishes.

What this stage covers

Performance and quality monitoring against the measures agreed at build time
Ongoing evaluation as models, prompts, and source data change
Cost and usage analysis, with optimization where it is worth doing
Model and workflow tuning
Incident and exception review
Support and maintenance
Governance reporting for internal stakeholders
New workflow development and quarterly value reviews

You end with a system that continues to earn its place, and a clear record of what it is producing.

Scope and investment

Engagements are scoped after a conversation, not from a price list.

What a piece of work costs depends on the workflow, the systems it touches, the data it depends on, and the controls the organization requires. We would rather scope accurately than quote quickly.

Each stage is scoped against the architecture and priorities agreed at the end of the one before it, so you are never estimating in the dark. If the work we would recommend is smaller than the work you asked about, we will say so.

Not sure which stage you are at?

Describe the workflow that is costing you the most and we will tell you whether it calls for an assessment, a pilot, a direct implementation — or none of the above.

Discuss an AI Opportunity Explore Our Approach