Orchard / Our thesis
The next AI investment starts
with knowing which work to change.
AI makes more software and automation possible. Organizations still need to understand which work deserves attention, what exceptions matter, and whether a change helped. That is the problem Orchard is built around.
Why this matters now
More ways to build.
A harder question
about what to build.
A team can introduce AI and still rebuild the same report, repeat the same handoff, or rely on an undocumented workaround. Choosing where to invest requires a closer understanding of the work itself.
McKinsey’s August 2026 survey reports wider AI adoption alongside a persistent gap between individual productivity gains and enterprise financial impact.
Read the research behind this contextExternal market research. This is context for our thesis, not evidence of Orchard results.
The product connection
Keep the question,
the evidence, and
the next step together.
Orchard connects observed work activity with Isaac, Orchard’s AI, Studio reports, recorded procedures, and configured workflows.
A manager can investigate a repeated task, inspect the source, and give the team a procedure to review. The same finding can support training, a process change, or a tested automation.
Follow the product story
A focused starting point
An operations question.
An accountable sponsor.
We are building for operations and IT leaders in established organizations where computer-based work spans departments and applications. The starting point is a team with a recurring task or software-use question, an accountable sponsor, and a question existing reports leave unanswered.
The first evaluation should establish whether the evidence changes a decision. A useful next step might be a clearer procedure, better-targeted training, or less repeated handling. Assess the result against the team’s own measures of quality, turnaround, and rework.
Commercial direction
Start with a useful decision.
Earn the next one.
Our proposed model is recurring platform access for an agreed organization or business unit, with clear capacity, data, AI usage, and support allowances.
Explore proposed pricing & scope- 01
Prove usefulness in one team
Choose a task, an owner, and an outcome measure. Evaluate the quality of the findings and the effort it takes to get them.
- 02
Return as the work changes
New tools, new handoffs, and new team members create new questions. Continued use should support those decisions.
- 03
Expand where the evidence supports it
Add teams or explore partner delivery after assessing value, deployment effort, and responsibilities. Expansion is earned through usefulness.
Company & investment conversations
See the product.
Discuss the opportunity.
Talk with us about the customer problem, the product we’re building, and our commercial direction.
The examples on this site use sample data. They are not customer results or evidence of commercial traction.