Methodology, not abstractions.

Four phases. Pick the one that fits.

Most clients enter through one of four engagement shapes. The diagnostic is the typical front door. The sprint and the app build are scoped implementations. The retainer is what runs after. Pricing depends on scope — we share it on the first call.

• // 01

  Operations Diagnostic — 2 weeks

  Workflow audit, opportunity ranking, and a written recommendation. The right starting point when you're not sure where AI fits or where to spend.

• // 02

  Workflow Sprint — 3 weeks

  Scoped, single-workflow implementation. Right when the team has clarity on what they want and just needs it built.

• // 03

  Innovi AI Apps — weeks, not quarters

  A purpose-built application: internal operations, improvements to software you already run, or a legacy platform moved to the cloud.

• // 04

  Fractional AI Engineering — monthly retainer

  Ongoing operations after deployment. Right when the system is live and the team needs senior AI engineering on retainer.

1 — Operations Diagnostic.

Two weeks. Before recommending anything, we understand what's actually happening.

Week 1 — Discovery. We talk to the people doing the work, not just the project sponsor. We watch the workflow. We read the existing documentation. We pull the data the system would need to use.

Specific deliverables: a written summary of the current workflow, the people involved, the systems they use, the volume and velocity of work, and the time spent on each step.

Week 2 — Recommendation. We write a structured recommendation document. Five sections: what we observed, what the highest-value AI opportunity is, what's required to do it well, what it would cost (engagement plus ongoing), and what could go wrong.

The deliverable is eight to fifteen pages. It's specific. It's yours to keep regardless of whether you continue with us.

2 — Workflow Sprint.

Three weeks. A scoped, single-workflow implementation.

Week 1 — Design and integration setup. We confirm the workflow with end users (one half-day workshop). We set up access to the systems we'll integrate with. We provision the cloud resources. We build the first end-to-end skeleton — the AI works on test data, integrated with the real systems.

Week 2 — Build and tune. We build the production implementation. We construct an evaluation suite from real historical data. We tune until quality thresholds are met. We instrument cost and quality monitoring.

Week 3 — Pilot, train, hand off. We run a one-week shadow-mode pilot. The AI runs in production but its output isn't acted on. We compare to what the team actually does. We tune based on the gap. We train the team. We hand off the runbook.

End state. A working system, an evaluation suite, monitoring dashboards, a runbook, and trained operators.

3 — Innovi AI Apps.

Weeks, not quarters. When the answer isn't a single workflow but an actual application — something your team opens every day. We use AI tooling across the build, which is what makes the timeline possible: a working first version lands early and earns its keep while the rest is still being built.

Three areas. An engagement is scoped to one of them.

Internal operations

The target is the process running on spreadsheets emailed between people, on a handful of SaaS tools that each do part of the job, or on a manual routine nobody has had time to replace. We build the application that covers it end to end — intake, approvals, tracking, reporting — and design the handoffs out.

AI goes only where it earns its place: pulling structured data out of documents and email, classifying and routing work, drafting the repetitive writing, and flagging the exceptions a person should actually look at. The rest stays ordinary software, because ordinary software is more predictable and cheaper to run.

End state. A deployed application, integrations into the systems you already run, a written runbook, and a trained team.

Existing software improvements

For products and internal systems that work but have aged — dated interfaces, a stalled roadmap, and a change cost that climbs every release. We audit what's there, agree a prioritized sequence, and deliver against it. We keep what works and replace what's holding you back, rather than asking you to fund a full rewrite before seeing anything.

Here AI mostly accelerates our own work: reading unfamiliar code, generating the test coverage that should have existed, and handling mechanical refactors. Where it fits the product itself, we add AI features to what you already run.

End state. A current-state audit, a sequenced plan, and delivered improvements against it.

Legacy platforms to the cloud

For applications still sitting on bare-metal servers or legacy virtualization, where the hardware is aging, licensing renews whether or not it's used, and the people who set it up have moved on. We move them to the platform you choose — Microsoft Azure, Google Cloud, or AWS — rearchitecting where it's warranted and leaving the rest alone.

We don't have a house cloud. Where it makes sense, we assess all three against your actual workload and combine the strongest components across them rather than defaulting to one. See how we choose cloud and model providers below.

End state. A migration plan with a cost model, the move executed in stages, and infrastructure documented well enough that your team can run it without us.

4 — Fractional AI Engineering.

Monthly retainer, three-month minimum. The retainer that runs after the initial engagement.

Monthly check-ins, prompt updates as the business changes, model upgrades as providers ship new ones, evaluation suite maintenance, monitoring oversight, and incident response.

Most clients move into a retainer within sixty days of going live, because AI systems quietly drift if nobody is watching them.

How we choose cloud and model providers.

We are deliberately not cloud-specific. We build production systems on Microsoft Azure, Google Cloud, and AWS. We use Anthropic Claude, OpenAI GPT, Google Gemini, and open-source models depending on the task.

The right cloud and model choice for any given engagement depends on five things:

• Where the client's data already lives
• What compliance requirements apply (HIPAA, SOC 2, FedRAMP, GDPR, etc.)
• What the existing tech stack looks like
• What the team can operate after we leave
• What the workload economics require

We make this choice transparently in the diagnostic phase, with the trade-offs written down. We're not paid by any provider to recommend one over another.

What every engagement includes.

Regardless of phase, every engagement includes:

An evaluation system. A scored test suite running on real historical data, with quality thresholds defined up front. Without this, "is it working?" has no answer.

Production-grade integration. The AI lives inside the tools the team already uses. No copy-paste workflows.

Cost and quality monitoring. Dashboards from day one. The CFO knows what each transaction costs. The operations lead knows whether quality is holding.

A runbook. A written document the client owns forever, describing how to operate, monitor, update, and troubleshoot the system. Includes how to call us for help when needed.

Training sessions. Real training, not a 30-minute demo. Operators, managers, and technical caretakers each get their own session.

What we don't do.

We don't do strategy decks without implementation. We don't do AI training programs. We don't do reseller arrangements for someone else's software. We don't do "AI readiness assessments" that take six months and produce a PowerPoint.

We build, ship, and operate.