Service · Workflow Implementation

One workflow in production.
Governed. Integrated. Done.

We redesign one workflow at a time and build the thin slice that proves the operating model: prompts, data access, human review gates, integrations, and the runbook needed for real production use.

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What this solves

You have a workflow identified. Now you need it running in production: not in a sandbox.

Most AI pilots fail at the handoff point: the model works in isolation but the integration is missing, the human review gates aren't designed, or the rollout lacks the runbook needed for real adoption. Otonmi builds one workflow slice all the way to production: with governance, integrations, and trained owners.

⚙️
Production Deployment
One workflow slice deployed in your production environment: real integration, real data, real users, with measurement against the baseline established at the start.
👥
Human Review Design
Explicit design of where human judgment stays in the loop: escalation paths, review checkpoints, exception handling, and role clarity. Built in, not bolted on.
🔒
Governance Controls
Prompt framework, evaluation criteria, observability setup, and control points: so the workflow is auditable and improvable from day one.
📝
Rollout Playbook
A plain-language operator runbook, adoption plan, and architecture blueprint for expanding to the next workflow in the domain.
How we build

We redesign the work before we automate it.

Every implementation engagement follows the Aizen™ methodology: map the current workflow, identify human and AI roles, build the thin slice, add controls, deploy with enablement. Six weeks. One production system.

Current-State Mapping
We observe and document the workflow as it actually runs, not as documented. This establishes the baseline for measuring what AI actually changes.
Human + AI Role Design
We define exactly what AI executes, what it assists, and what stays fully human-led. Escalation paths, review checkpoints, and exception routing are designed before any code is written.
Thin Slice Build
We build on your existing stack: no new platforms required. Integration architecture, prompt engineering, data access patterns, and evaluation framework are established in the first two weeks.
Controls & Observability
Audit logging, quality thresholds, monitoring scaffolding, and governance controls are added before production deployment: not as a retrofit.
Production Deployment & Enablement
We deploy into your production environment, train the operators, document the runbook, and establish the performance baseline. The system runs. Ownership belongs to your team.
What we work on

Workflow families where governed AI delivers the most value.

📂
Casework & Intake
Triage, routing, document extraction, policy lookup, and exception handling: high volume, rule-intensive, and slow when done manually.
🔍
Knowledge & Policy Workflows
Internal search, answer generation, decision support, and guidance retrieval with human review: where the risk is wrong answers, not slow answers.
👥
Operational Coordination
Service requests, approvals, task orchestration, and cross-system follow-up: where coordination drag costs hours per day across multiple teams.
✅
Compliance-Aware Automation
Workflows where auditability, review checkpoints, escalation paths, and policy alignment must be designed in: not added after go-live.

One workflow in production.
Six weeks. Fixed price.

Real integration. Real data. Real users. Human review built in. Governance controls from day one. Not a sandbox proof of concept.

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Workflow Value Sprint
2 weeks · prioritize & roadmap
$18k–$35k
Production-Ready Thin Slice
6 weeks · one workflow in production
$60k–$125k
Domain System Launch
10–12 weeks · full domain live
$140k–$300k
Common questions
What AI models and platforms do you work with?
We are platform-neutral. We work with the models and infrastructure already in your environment: or recommend the right fit based on your workflow's data sensitivity, latency requirements, and cost profile. We have experience with Claude, GPT-4o, Gemini, Llama, and cloud-managed inference services.
Do you build custom models?
Rarely necessary. Most workflow implementations use prompt engineering, retrieval, and integration work on top of existing foundation models. We only recommend custom fine-tuning when domain specificity, latency, or data sovereignty requirements genuinely require it.
How do you handle sensitive data?
Data handling is part of the governance design in every engagement. We work within your existing data boundaries, help design appropriate data access patterns, and flag where PII, sensitive documents, or regulated data require specific controls before the build begins.
What proves value at the end?
We establish a baseline KPI model before building. At deployment, we measure against that baseline: cycle time, throughput, error rate, or capacity freed, depending on the workflow. Proof is tied to the metrics your organization cares about, not generic benchmarks.

Done piloting.
Ready to deploy?

One workflow. Six weeks. In production: with governance controls, human review, and trained owners.

Book a Working Session →