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AI Workflow Automation & Data Readiness
Turn manual workflows into production AI systems — without the guesswork.
We help mid-market teams figure out what’s actually ready to automate, build it, and keep it running. Every phase has a clearly defined scope, so you always know exactly what you’re getting before it starts.
Why Most AI Projects Stall
The build usually isn’t the hard part — knowing what’s ready to build is.
Most organizations don’t know which manual processes are actually good candidates for automation, or whether the data behind them is clean and structured enough for a language model to read reliably. Skipping straight to a build means paying to discover that halfway through the project — instead of before you commit to it.
The Engagement Model
Three phases, each one scoped on its own.
Most clients move through all three phases because each one only makes sense once the last is done, but you can stop after any of them.
Data Readiness & Workflow Diagnostic
A 2–4 week audit that maps your manual workflows, checks whether your data is structured enough for AI, and hands you a prioritized roadmap with ROI estimates.
Explore Phase 1 Phase 2Workflow Implementation Sprint
A 4–8 week sprint to build, test, and deploy the workflow the diagnostic identified — from a single SaaS-to-SaaS connection to a multi-system integration.
Explore Phase 2 Phase 3Managed AI Operations
Ongoing monitoring, prompt tuning, API maintenance, and exception handling so the workflow keeps running as APIs change and models drift.
Explore Phase 3Engagement Summary
What each phase delivers.
| Engagement Phase | Core Deliverable |
|---|---|
| Data Readiness Diagnostic | Workflow mapping, data-structure audit, ROI roadmap. |
| Workflow Implementation | Scoped build, API integration, testing, deployment. |
| Managed AI Operations | Ongoing API maintenance, prompt tuning, exception handling. |
| Hourly Advisory Optional | Strategic consulting outside a defined project scope. |
Complex multi-system builds involving legacy ERP integration, data clean-up, or compliance protocols scope larger than a single SaaS-to-SaaS workflow. Exact scope is set during the diagnostic, once we know what we’re actually integrating with.
Why We Start With a Diagnostic
Discovery work has real value, and we treat it like real work.
A free consultation is a sales call dressed up as advice. The diagnostic is a work product — a workflow map, a data-readiness assessment, and an ROI-ranked roadmap you can act on with any partner, whether or not you move into the implementation sprint with us.
Why It Doesn’t Stay Fixed Once It’s Live
Automations don’t fail at launch — they fail three months in.
APIs Change
Vendors update endpoints, deprecate fields, and change rate limits without much warning. A workflow built against last quarter’s API can quietly stop working.
Models Drift
The model behind a prompt today isn’t guaranteed to behave the same way after a provider update. Output quality needs to be checked, not assumed.
Edge Cases Surface
Real-world data eventually sends a workflow something it wasn’t built to handle. Someone needs to catch that, fix it, and keep the system trustworthy.
The Alternative to Hiring
Ongoing coverage versus a new hire.
Managed AI Operations gives you continuous coverage without a recruiting timeline, a ramp-up period, or the risk of your automation knowledge sitting with one person who might leave.
Frequently asked questions
Answers to common questions about how these engagements work
What do you actually deliver during the diagnostic?
A written workflow map, a data-readiness assessment, and a prioritized, ROI-ranked roadmap you can act on. It’s a real work product you keep, not a sales pitch dressed up as a consultation.
What does the implementation sprint actually produce?
A production-ready AI workflow: built, tested against edge cases, deployed, and documented so your team can maintain it — not a prototype or a demo.
What happens after a workflow goes live?
APIs change, models drift, and edge cases surface once real volume hits a workflow. Managed AI Operations is a monthly retainer that covers monitoring, prompt tuning, API maintenance, and exception handling so the systems we build keep running without you needing an in-house AI engineer on call.
Do you only work within Salesforce and Palantir?
Our Salesforce Agentforce and Palantir Foundry work is deep, platform-specific engineering. AI Workflow Automation is a separate, platform-agnostic service line for teams that need a specific manual process automated — CRM-to-LLM summarization, intake processing, or legacy ERP integration — regardless of what systems they run today.