Shared plan as input
Update the codeStart with the findings. Spend effort on the assigned task.
Governance for enterprise AI
The AI ecosystem
for the enterprise.
Compliance controls. A smaller AI bill, or more work from the same one.
Connect people, agents and tools across the organization. Apply enterprise policies, preserve shared knowledge and keep AI work on the record.
7 days free, then $49.99/month for 6 months and $100/month after. Card required; cancel anytime.
Build the business case
Estimate enterprise AI savings.
Model the cost of repeated AI work across a team. Take what Orchard removes as a smaller bill, or keep the bill and get more work done. Model spend only — no staff time.
Workload assumptions
Take the savings as
Estimated monthly savings$3,811.71
$45,740over twelve identical months
About 20% less modeled AI spend for 25 people. After 5% coordination overhead. Orchard pricing is not included.
Spend avoided$4,533.38−Coordination cost$721.67=Net savings / month$3,811.71Amounts rounded; totals use unrounded values.
Where the savings come from
Where the savings come from
Removing 50% of each selected source of repeated work. Monthly savings before coordination costs.
- Sending more than the job needs$1,856.25
- Answers longer than the question$515.63
- Re-reading work to check it$1,237.50
- The same problem solved twice$268.13
- One change erasing another$136.13
- Building before the approach is agreed$519.75
- Repeated spend avoided
- $4,533.38
- Less coordination overhead
- -$721.67
- Net monthly savings
- $3,811.71
Category amounts are rounded; totals use unrounded values.
See the full bill comparison
Your AI bill, without Orchard
With 50% of the repetition removed
$15,155- Work you wanted
- The same work, bought again
- Coordination overhead
| Category | Your AI bill, without Orchard | With 50% of the repetition removed |
|---|---|---|
| Work you wanted | $9,900.00 | $9,900.00 |
| The same work, bought again | $9,066.75 | $4,533.38 |
| Coordination overhead | $0.00 | $721.67 |
Both bars use the same dollar scale. The work you wanted does not move: doing the job once costs the same either way. Only the repeated half is in play.
Modeled AI spend / person / month$759Repeated share of that bill48%Billed tokens avoided / month734.7M
Check the modeled bill against a real invoice before you trust the rest. If it is too high or too low, change the tasks per day until it matches.
Customize sources of repeated work
Six ways the same job gets bought twice
These categories feed the calculator above. Each amount shows the spend avoided at the selected 50% reduction, before coordination costs. Turn a category off to remove it from the scenario.
25 people · 15 tasks per person / day · Claude Opus 5 · 50% reduction · 5% overhead
- $1,856.25avoided / month
$3,712.50 repeated spend × 50% reduction
Every step drags along sprawling files and old conversation the task never uses. You are billed for all of it, on every step.
Smaller files · shared memorySmall, single-purpose files and a shared record of what was already learned, so a step carries the part that matters.
- $515.63avoided / month
$1,031.25 repeated spend × 50% reduction
Restated background, narration and padding. Generated text is the most expensive thing you buy, and this is the part nobody asked for.
Tight responsesResponse rules that return the decision and the change instead of describing the work along the way.
- $1,237.50avoided / month
$2,475.00 repeated spend × 50% reduction
With no automated check, the model is asked again to confirm its own work, re-reading the same material to answer a question a test could settle.
Test plans · CI gatesA test plan travels with the change and the pipeline runs it on every push, so the answer comes from CI instead of another paid round.
- $268.13avoided / month
$536.25 repeated spend × 50% reduction
Two workers investigate the same thing in parallel. Both read the same code, both reason it through, and you pay for both.
CoordinationAssigned scopes and a shared record of findings, so the second one starts from the first one’s result instead of repeating it.
- $136.13avoided / month
$272.25 repeated spend × 50% reduction
A second change lands on top of the first and removes it. It surfaces later, and the whole investigation and fix is bought again.
No overwritingDeclared ownership and a check before writing, so two workers never edit the same file blind.
- $519.75avoided / month
$1,039.50 repeated spend × 50% reduction
A full build runs on the wrong approach and is thrown away. The bill covers the discarded attempt and the real one.
Proposal first · predict then implementA short written proposal and a predicted result, checked while changing course still costs a paragraph instead of a build.
Spend avoided$4,533.38−Coordination cost$721.67=Net savings / month$3,811.71Amounts rounded; totals use unrounded values.
Category savings total $4,533.38; subtract $721.67 in coordination costs to get the same $3,811.71 monthly estimate shown above. Priced at published list rates for the model selected. None of this is a measured Orchard result: it is your own workload, costed against the practices Orchard enforces.
Inspect the math, pricing and assumptions
Monthly opportunity = (people × tasks per day × working days) × the share of tasks each source touches × its repeated tokens at list price, × the share you expect to remove, − what coordination costs you.
| Source of repeated work | % of tasks | Repeated input tokens | Repeated output tokens |
|---|---|---|---|
| Sending more than the job needs | |||
| Answers longer than the question | |||
| Re-reading work to check it | |||
| The same problem solved twice | |||
| One change erasing another | |||
| Building before the approach is agreed |
| Model | Input | Output | Cache read |
|---|---|---|---|
| Claude Sonnet 5 | $2.00 | $10.00 | $0.20 |
| Claude Opus 5 | $5.00 | $25.00 | $0.50 |
| GPT-6 Astra | $10.00 | $50.00 | $1.00 |
The default task is a multi-step agent session, not a single question: an agent reads, runs something, reads the result and continues, and every step re-sends the context so far. That is why per-task token counts run to the hundreds of thousands. Tokens are priced at uncached list rates, so the estimate is conservative for teams already getting cache hits; provider caching is a separate saving. GPT-6 Astra uses its short-context tier. Equal token counts compare prices, not model quality or equal work.
These are API-equivalent costs. On a subscription, avoiding work may preserve your usage allowance without changing the invoice. Annual figures repeat the same month twelve times; they are not a forecast. Staff time, tools, infrastructure, taxes and provider discounts are outside the model, and nothing here is a measured Orchard result.
Shared recordIllustrative
Agent activityAccount · machine · session
CapturedA finding to reviewOriginal evidence attached
ReviewA source not reportingCoverage gap stays visible
GapCompliance
Enterprise policies.
One shared record.
Apply organizational policies to connected agents. Review captured actions across accounts, machines and sessions, follow findings to their evidence, and identify coverage gaps.
Controls and visibility depend on the runtime and configured coverage.
Who we are
Operators who have built, scaled and sold companies before.
Orchard is a New York team of founders, engineers and specialists with decades of experience in health tech, cybersecurity and compliance software, including former roles and contract work with AWS, Google and Microsoft.
Leadership has built and sold multiple companies. We are building Orchard for the regulated environments we know best: FedRAMP, SOC 2, NIS2 and DoD.
Meet the team- Decades
- of founder and operator experience
- Multiple exits
- worth hundreds of millions
- AWS · Google · Microsoft
- former roles and contracts
- New York City
- headquarters
Policy before action
Approved work moves.
Risk stops here.
One control point for enterprise AI. Give approved work a clear path. When a request crosses a policy boundary, stop it and put the decision in front of the right people.
Discuss enterprise controls01 / 08Checking
An action reaches the gate.
Claude Code requests access. Orchard checks the enterprise policy.
Request · Claude Code
Read the published database schemaApproved schema catalog
Claude CodeAction requested
CodexReady to send
DeepSeekReady to send
Managed machineAwaiting authorization
DATA-07 · Approved data access only
DATA-07 Production data access
Production customer data must go through the approved data service. Published schema access is permitted. This sample rule applies to every agent.
01
Decision recordedA reason for every decision
Machine AI restrictedContain the rejected action
Notify personnelKeep the responsible team informed
Illustrative sequence, not a live incident. Current machine enforcement covers enrolled Claude Code and Codex hooks. DATA-07 is a sample rule; DeepSeek gating and the personnel email sequence are shown as a product concept.
Explore agent coordinationTeam coordination
One connected ecosystem.
People, agents and shared knowledge. Select a node to follow the connections.
Scroll or swipe inside the graph to pan. Select a node to inspect its routes.
Better together
Shared knowledge. Coordinated execution.
One team’s research can become the next agent’s starting point. Explore how shared context changes a multi-agent workflow.
Generate useful output once
A reviewed migration plan
A capable model maps the constraints. The team checks the plan and shares it.
Reuse the findings as input
Shared plan as input
Check the behaviorStart with the findings. Spend effort on the assigned task.
Shared plan as input
Update the runbookStart with the findings. Spend effort on the assigned task.
Shared reasoning gives each agent a head start. Models can be chosen to fit each task.
Illustrative workflow. Savings depend on the work avoided, model pricing and the context carried forward.
Contact sales
Plan an enterprise
AI rollout.
Discuss governance requirements, operating scale and AI cost priorities with the Orchard team.
The conversation starts with requirements, a platform walkthrough and a scoped rollout plan.
Discuss an enterprise rollout.
What is Orchard?
Orchard is an enterprise AI platform and services company. It connects people, agents and tools across an organization with workspace policies, shared context, activity evidence and cost visibility.
How does Orchard support compliance?
Orchard provides workspace controls and recorded evidence for compliance review. Coverage and enforcement vary by runtime and configuration. It does not provide organizational certification or guarantee a compliance outcome.
How do we get started?
Start with the organization’s AI footprint, governance requirements and cost priorities. The Orchard team will discuss deployment scope, supported runtimes and engineering support.