Services

Five ways to put AI to work — all measured the same way.

Every engagement starts from a workflow, carries success criteria from day one, and ends with something your team actually uses. Start narrow; scale what earns its place.

AI Strategy & Opportunity Mapping

Find the work AI should actually be doing.

Who it's for: Operations, procurement, and corporate services leaders who are being asked "what is our AI plan?" and want an answer grounded in their actual workflows.
The problem: Most AI roadmaps start from the technology. The result is pilots nobody uses and a backlog of ideas with no ROI logic behind them.
What we deliver: A workflow-level map of where AI pays off in your operation — sized by hours, volume, and risk — with a prioritized build plan leadership can defend.

Example outputs

Opportunity map across your workflows, ranked by value and feasibility
ROI sizing per use case: hours, volume, error cost
Build / buy / wait calls with honest reasoning
A 90-day plan for the first one or two builds

Typical first engagement

A 2–4 week discovery sprint across one function — for example procurement, supply chain, or corporate services.

How success is measured

Use cases sized and ranked, not just listed
A first build selected with agreed success criteria
Leadership alignment on where AI does — and does not — pay off

AI Agents for Procurement & Operations

Agents for the work your experts should not be doing manually.

Who it's for: Procurement, sourcing, category management, and supply chain teams whose expert hours go to high-volume manual work.
The problem: Specialist time is spent gathering, formatting, summarizing, and chasing — not negotiating, deciding, and managing suppliers.
What we deliver: Focused AI agents for Source-to-Contract and operational workflows: market and vendor research, category strategy support, tender and contract document work, spend and reporting analysis.

Example outputs

Category strategy research and drafting agents
Vendor and market intelligence briefs on demand
Tender / RFP document preparation and analysis support
Contract review and summarization assistants
Spend, savings, and compliance reporting agents

Typical first engagement

One workflow, one agent, tested against your real historical cases — typically 4–8 weeks.

How success is measured

Hours returned per cycle, measured against the manual baseline
Cycle-time reduction on the target workflow
Output quality graded by the experts who own the work
Sustained adoption by the working team

Internal Tools & Workflow Automation

Purpose-built tools for the work that never stops.

Who it's for: Teams whose week is consumed by repetitive document, data, and coordination work.
The problem: The work is too specific for off-the-shelf software and too constant to keep doing by hand.
What we deliver: Internal tools and automations built around your actual process — intake, triage, drafting, checking, reporting — with AI where it helps and plain software where it does not.

Example outputs

Knowledge assistants over your documents, policies, and precedents
Intake and triage tools for high-volume requests
Report and summary generators on your templates
Checklist, compliance, and QA automation

Typical first engagement

A working internal tool for one team, shipped in weeks.

How success is measured

Manual steps removed from the process
Turnaround time from request to output
Team hours per week returned

Prototype-to-Production Builds

Working systems, not another deck.

Who it's for: Leaders with a specific idea who need working evidence — not a feasibility study.
The problem: Ideas die in slideware. Committees debate feasibility that a two-week build would settle.
What we deliver: A working prototype run against real data and real cases, fast — then a hardened path to production for what earns it: data boundaries, access control, monitoring, handover.

Example outputs

A working prototype in weeks, not quarters
Evaluation against real historical cases with pass/fail criteria
Production hardening plan: data boundaries, access, monitoring
Handover documentation and team training

Typical first engagement

A 2–6 week prototype sprint with agreed success criteria up front.

How success is measured

Prototype performance against the agreed criteria
A clear build / kill decision, backed by evidence
Time from idea to working evidence

AI Readiness, Governance & Adoption

AI that survives security review and daily use.

Who it's for: Organizations that need AI to pass security review, procurement scrutiny, and — hardest of all — actual daily use by the team.
The problem: Pilots pass demos and fail rollout: data boundaries unclear, review steps undefined, teams untrained, value never measured.
What we deliver: The operating framework around the tools: data-sensitivity boundaries, human-in-the-loop review points, usage guidelines, training, and a measurement approach that shows whether the value is real.

Example outputs

Data boundary and access rules per workflow
Human review points and escalation paths, designed in
Usage playbooks and team training
Measurement framework: hours, quality, adoption

Typical first engagement

A governance and adoption layer added to your first build — or an honest review of what you already have.

How success is measured

Security and compliance sign-off
Sustained weekly usage after rollout
Measured value at 90 days, not launch-day enthusiasm

Built for enterprise reality

The unglamorous parts, done properly.

Enterprise AI fails on governance, adoption, and trust more often than on models. We treat those as part of the build, not an afterthought.

Data boundaries first

Sensitive data is mapped and fenced before any build starts. What cannot leave your environment, does not.

Humans decide

Agents draft, gather, and check. People review, approve, and decide. Review points are designed in, not bolted on.

Measured, not assumed

Every build carries success criteria and a measurement plan from day one. If the value is not there, we say so.

Adoption is the product

Tools are built into how the team already works — with training, playbooks, and rollout as part of the engagement.

Governance that survives review

Access control, logging, and escalation paths built to pass security and procurement scrutiny — we have sat on that side of the table.

Honest scoping

If AI is the wrong tool for a workflow, we tell you — and recommend the boring fix instead.

Start with one workflow

Send us the process that is costing your team time.

Describe one workflow — what goes in, what comes out, who touches it, and how long it takes. We will tell you honestly whether AI is the right tool, and what a first build would look like.

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