Discover. Design. Deploy. Handover.
Every engagement follows four steps: learn the workflow, design the integration and approval plan, build and ship to production, then hand over a system you own. Clean integrations take days. Complex environments get a longer Discover phase.
What You Bring. What We Build.
You provide the context. We deliver the working system.
Discover
You Provide
- Current process walkthrough
- Tools and logins in use
- Team structure and approval chains
We Deliver
- Workflow map with trigger, steps, and outputs
- Integration readiness assessment
- ROI estimate tied to your KPI
Design
You Provide
- Tool access and API credentials
- Approval rules and thresholds
- Cap preferences (runs per day, emails per cycle)
We Deliver
- Signed-off requirements and scope
- Integration plan with data contracts
- Configured approval gates in your team chat
Deploy
You Provide
- Feedback during parallel run
- Final sign-off on production readiness
We Deliver
- Production deployment with monitoring
- Exception playbooks and alerting
- First ops report
Handover
You Provide
- Team availability for walkthrough
- Questions and edge cases
We Deliver
- Documentation and command reference
- Approval workflow walkthrough
- The running system, fully owned by you
7-Day Milestone Calendar
Every day has a clear deliverable. No ambiguity, no scope creep.
Process mapping and tool connection
Automation logic and approval gate design
Exception handling and edge case configuration
Integration testing and cap configuration
Monitoring setup and alert configuration
Parallel run, automated alongside manual
Go-live with full monitoring active
When the Landscape is More Complex
Not every environment is a 7-day build. Some require groundwork before the first workflow ships.
The 7-day timeline assumes clean integrations: tools with documented APIs, consistent data formats, and straightforward approval chains. Many organizations meet this description.
Some do not. Legacy systems with inconsistent schemas, data spread across multiple ERPs, or field names that mean different things in different business units all require groundwork before automation. For these environments, the Discover phase expands into a dedicated discovery engagement. We audit the data landscape, map integration points, and scope the work before anything is built.
The Design and Deploy phases work the same way. The guardrails are the same. The handover is the same. Only the timeline adapts to the complexity of the environment.
Definition of Done
Your workflow is not "done" until every item on this checklist is checked off.
- Workflow running in production
- Approval gates configured and tested
- Hard caps set and verified
- Monitoring and alerting active
- First dry run passed
- Documentation and command reference delivered
The operational layer that makes it production
Every demo looks like magic. Then you connect it to real data, real users, and a real budget. We plan for all three.
Latency budgets
Every step in a workflow adds time. Pulling invoices, categorizing them, drafting emails, posting notifications. Chained together, a workflow that felt instant in a prototype can take 3 to 8 seconds end to end. We design every workflow with a latency budget. Before we build, we map the step count and estimate the total chain time. If it exceeds the threshold, we restructure: parallelize independent steps, pre-cache reference data, or split into a fast path (notification) and a slow path (full analysis).
Cost tracking
AI inference uses compute. Running categorization across 200 invoices daily, generating personalized emails, and scoring deal health consumes GPU and memory cycles on your hardware. Every workflow ships with a resource model baked into the implementation plan. The system tracks compute usage per workflow run. Hard caps pause the workflow rather than letting it consume unbounded resources. Usage metrics are logged in the audit trail so you can see exactly what each run costs in compute time.
How Autonomy Expands
We do not hand you full automation on day one. Trust is built in phases.
Draft and recommend
The workflow drafts outputs (emails, reports, updates) but does not send or publish anything. Your team reviews every draft before it goes out. This is where your team learns what the workflow does and starts trusting it.
Execute low-risk tasks
Reporting, scheduling suggestions, internal summaries, data assembly. Work where a mistake is easy to catch and costs nothing to correct. The workflow starts doing things on its own, with full audit logs.
Execute in money flows
Invoice follow-ups, payment reminders, reconciliation entries, dunning sequences. Work that touches finances, with approval gates on every high-value action. Nothing goes out without a human confirming it.
Supervised autonomy
The workflow handles the full process end-to-end within the guardrails you set. Your team supervises instead of operating. The audit log shows everything the workflow did, and you adjust boundaries based on what you see.
Your team goes from operators of software to supervisors of workflows. The speed of this transition is entirely up to you.
Built-In Risk Controls
These are not afterthoughts bolted on after the build. They are designed into the implementation from day one. The guardrails are the reason a client can trust the system to run without constant supervision.
Human approval gates
Configurable thresholds that route decisions to a person before the workflow acts. The controller approves collections emails over a dollar amount. The bookkeeper signs off on extracted invoice data before it posts to your accounting system. The sales manager reviews flagged at-risk deals before the team sees the digest.
Audit logs
Every workflow run produces a trace: what triggered it, what data it processed, what decisions it made, what outputs it produced. When someone asks "why did this email go out?" or "why was this invoice not flagged?", the audit log answers with specifics, not guesses.
Hard caps
Workflow runs per day, emails sent per cycle, compute hours per month. Caps prevent a misconfigured trigger or an unexpected data spike from overloading your hardware or flooding an inbox. When a cap is hit, the workflow pauses and notifies. No silent failures, no surprise bills.
Ongoing evaluation
Traditional software ships, passes tests, and works until someone changes the code. AI workflows do not behave that way. A model update, a new invoice template, a renamed CRM field, or seasonal ticket shifts can all degrade quality. Every workflow ships with reference inputs that can be replayed to verify outputs have not drifted. The operations plan includes monthly regression testing and performance comparison against the go-live baseline.
Ready to start?
We will map your workflow, estimate savings, and give you a straight answer on fit.