Rivergreen

Working software every few weeks, or we're not doing it right

Every stage of a Rivergreen engagement ends with something running. Here's what that looks like week by week — and the engineering standards that make it hold up in production.

How we work

Four stages, in order. Each one ends with something your team can see, use, or run.

  1. 1Align

    Executive alignment

    A half-day with your leadership team. We agree where AI matters, where it doesn't, and what 'working' means this year — because a company moves at the speed of its leaders.

  2. 2Map

    Map the work

    We interview the people doing the work, map how it actually moves between systems, and rank every opportunity by value and feasibility. The best one becomes a working prototype.

  3. 3Build

    Build the systems

    Six-week sprints ship production agents and workflows on the stack you already run — with an eval suite, approval gates, and monitoring. Not a demo.

  4. 4Train

    Train the team

    Your champions pair with our builders from day one. Hands-on training and office hours make sure your team runs the systems — and keeps building — after we leave.

Afterwards, we can stay on as your fractional Head of AI.

AI Diagnostic · 3 weeks

What a Diagnostic looks like

  1. Week 1

    Listen and trace

    Interviews with the people who do the work — not just their managers. We map how work actually moves between systems, where it waits, and where it breaks.

  2. Week 2

    Score and shortlist

    Every candidate workflow gets a value, feasibility, and risk score with the reasoning written down. We start prototyping the top one while you review the long list.

  3. Week 3

    Prototype and readout

    A working prototype on your real data, a 90-day roadmap, and a readout for the executive team. You decide what to build first with something you can click.

Build Sprint · 6 weeks

What a Build Sprint looks like

  1. Weeks 1–2

    Scope lock and first slice

    System access, an eval set built from real historical cases, and a thin end-to-end version running by the end of week two.

  2. Weeks 3–4

    Full build, weekly demos

    Integration, edge cases, and the approval gates. Every week you see the system on real data with accuracy numbers — not a slide about progress.

  3. Weeks 5–6

    Harden, document, hand over

    Monitoring, cost controls, rollback plan, docs, and training for the owning team. Go-live is a supervised ramp: human review first, autonomy as the numbers earn it.

What every system we ship has in common

These aren't aspirations. They're in the scope of every Build Sprint, and they're why our systems are still running a year later.

  • Evals before features

    We define what 'working' means with a test set drawn from your real cases, and run it on every change. An automation that works most of the time is a demo.

  • Approval gates where it counts

    Any step that touches money, customers, or records of truth gets a human checkpoint until the system has earned autonomy — and a threshold that decides when.

  • Mostly code, a little model

    Models handle judgment and language; plain software handles everything else. Idempotent steps, safe retries, and durable execution keep it boring in production.

  • Observability from day one

    Every run is traced end-to-end. We watch accuracy, latency, and cost per task, and alert on drift before anyone on your team has to email us.

  • Your cloud, your data

    Systems deploy in your cloud or a dedicated environment. We use enterprise API terms that exclude training on your data, and document exactly what leaves your perimeter.

  • Handoff is a deliverable

    Documentation, runbooks, and a named owner on your side. If you can't operate and extend the system without us, we're not done.

We build with what you already run

We're not a reseller and we don't have a platform to push. Model choice follows the job; infrastructure follows your existing stack.

  • Claude
  • OpenAI
  • Gemini
  • AWS
  • Google Cloud
  • Azure
  • Snowflake
  • Postgres
  • Salesforce
  • HubSpot
  • Slack
  • Notion
  • Microsoft 365
  • Google Workspace
  • and whatever else you run

Models: Anthropic, OpenAI, Google, and open-weight models — routed by task, cost, and your data constraints. Agents and workflows are built in plain TypeScript or Python with durable execution, and we hand over the repo.

Ready to put AI to work?

Tell us what you're trying to do. We'll reply within one business day with a straight answer on fit and a time for a 45-minute intro call.

Work with us[email protected]