Workflow rescue
Take a useful prompt, spreadsheet, internal tool, or stalled AI prototype and make it reliable enough for day-to-day work.
Prototype hardening · system integration · production rollout
/ CUSTOM AI DEPLOYMENTS
/ BUILT IN YOUR ENVIRONMENT
/ PRICED BY SOLUTION — NOT SEAT
We turn a valuable workflow trapped in prompts, spreadsheets, or a fragile prototype into a production system your company can own.
Discovery, implementation, evaluation, and handoff — inside your approved environment, with people in charge of consequential decisions.
Enterprise solutions
No broad transformation program. One production target, one accountable owner, and a result the company can evaluate.
Take a useful prompt, spreadsheet, internal tool, or stalled AI prototype and make it reliable enough for day-to-day work.
Prototype hardening · system integration · production rollout
Turn document-heavy review into a repeatable workflow that assembles context, applies your rules, and routes consequential decisions to a person.
Intake · reconciliation · review packets · exception handling
Build focused tools around the work your company actually does — connected to approved data, measured against real examples, and owned by your team.
Research · operations · customer work · decision support
How deployment works
Nabu captures the objective, source material, operating constraints, owners, exceptions, and economic baseline. Open decisions stay visible instead of becoming assumptions in the build.
Shedu works with your workflow owner and technical custodian to implement inside your approved stack. Your data, credentials, code, and runtime remain under your control.
The finished workflow is tested against real historical cases, compared with the agreed target, documented, and accepted by the person accountable for the outcome.
What ships
A strong first engagement
Custom commercial model
Enterprise pricing reflects the workflow, integrations, deployment requirements, and evidence needed for acceptance. Start with one solution; expand only after it works in production.
Start with what is already working
We’ll determine whether it has a clear production target, what it will take to deploy, and how the result should be measured.