AI Workflow Automation that Ships, Audits, and Operates
from $40K. 6 to 8 weeks.
from $200K. 10 to 14 weeks.
from $80K. 8 to 12 weeks per workflow.
Where Do You Start?
Three situations we see most often. Pick the one closest to yours.
You're Moving a Manual Workflow into AI for the First Time
A workflow your team handles manually. Claims review, contract analysis, case triage, document classification. It has scaled past where headcount can keep up. You want to automate it properly, with audit trail, exception handling, and human-in-the-loop. Not a demo.
AI Use Case Discovery (6 to 8 weeks, from $40K). Then AI Workflow Production Build (10 to 14 weeks, $200K to $280K typical).
Production-deployed workflow on your infrastructure. Document ingestion and extraction. Human-in-the-loop review interface. Audit trail and decision logging. Compliance reporting. Operations runbook.
Your RPA Estate Has Plateaued
You invested in RPA and got real value early. The incremental return has slowed. Bots break on document variations, exceptions consume more time than they save, and the next generation of workflows requires understanding rather than rule-matching. Time to replace the bots with AI agents on the document-heavy nodes.
RPA-to-AI Workflow Modernization (8 to 12 weeks per workflow, $80K to $180K typical).
AI agents replace plateaued RPA bots on document-heavy nodes. Existing process flow preserved. Reduced exception handling time. Improved straight-through processing rate. Migration runbook for remaining RPA estate.
You Have an AI Workflow in Production That's Not Holding Up
An AI workflow you deployed is failing. Accuracy below target. Exception rate higher than the manual baseline. Compliance flagging decisions the audit committee can't defend. You need triage and rebuild, not a fresh assessment.
AI Workflow Remediation (4 to 6 weeks, premium pricing scoped to severity).
Root-cause analysis. Rebuilt extraction and decision layers. Restored audit trail. Migration of in-flight cases. Compliance documentation refreshed.
What a Typical Engagement Looks Like
Most clients arrive in one of the three situations above. The shape of a typical Build engagement is below. Modernize runs leaner because the workflow context is already understood. Remediate runs faster and surgically.
AI Use Case Discovery
- Weeks 1–3: Workflow inventory across in-scope operations. ROI ranking by effort and return. Top 3–5 workflows shortlisted with business sponsors.
- Weeks 4–6: Current-state process mapping for top 3. Target-state architecture for priority workflow. Build-vs-buy recommendation per workflow.
- Weeks 7–8: Business case with payback model. SOW for the production build, scoped and ready for procurement.
AI Workflow Production Build
- Weeks 9–11: Foundation build - document ingestion, extraction layer, and chunking. Working demo on synthetic data at end of sprint 1.
- Weeks 12–16: Decision logic, human-in-the-loop interface, exception handling. Accuracy testing against the target threshold.
- Weeks 17–20: Audit trail, decision logging, compliance reporting, integration with your systems of record.
- Week 21: Production cutover with pilot user group.
- Week 22: Handover documentation. Working session with your team.
Hypercare and Handover
- Senior engineers on call. Daily monitoring of accuracy, exception rate, and SLA performance.
- Operations runbook finalized against actual production patterns.
AI Optimization & Governance Retainer
- Monthly performance review against SLA baseline.
- Model and prompt drift monitoring.
- New workflow onboarding (1 per quarter typical).
- Compliance and audit support.
Typical first-year program investment: Discovery, Build, plus first year of Optimization and Governance retainer is $320K to $460K. Workflow-level ROI is calculated against hours saved and exception-rate reduction. Typical payback is 9 to 14 months.
Moving claims, contracts, or case work into production-grade AI with audit trail?
Who's on the Team
The senior practitioner who scopes the work is the senior practitioner who delivers it. Your team is named in the SOW.
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Lead Architect, Workflow & AI
15+ years in production AI and process automation. Owns workflow design, model selection, and engagement quality.
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Senior Engineer, Document AI
Deep on document ingestion, extraction, classification, and the model layer underneath. Writes the production code that determines straight-through processing rate.
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Senior Engineer, Integration & Compliance
Owns audit trail, decision logging, integration with your systems of record, and compliance reporting. The scaffolding your audit committee inspects.
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Platform Engineer
Cloud-agnostic deployment, infrastructure-as-code, observability, hypercare on-call rotation.
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Delivery Lead
Sprint cadence, fortnightly executive steering, RAID log, milestone acceptance. Single contact for engagement status.
How We Approach the Work
The workflow is the product, not the AI. Most "AI automation" engagements fail because they treat the model as the product. We treat the workflow as the product. AI is one component of a system that includes ingestion, extraction, human-in-the-loop, exception handling, audit trail, and compliance reporting.
Human-in-the-loop is a feature, not a fallback. We design the loop explicitly. Confidence thresholds, escalation paths, reviewer training, decision capture. Workflows that "go fully autonomous" typically come back six months later for rebuild after a compliance incident.
Audit trail is built in, not retrofitted. Every decision the system makes is logged with input, model output, confidence score, and the reasoning chain where applicable. Compliance can reconstruct any decision under audit.
Framework alignment is delivered with the system. NIST AI RMF, ISO 42001, EU AI Act, plus sector-specific (HIPAA, DORA, SEBI) are included in the documentation set. Not a separate engagement.
Technology and Platform Posture
You've already chosen your platforms. We deliver against your existing footprint. Below is what we work with most often.
Model Providers
Anthropic 
OpenAI 
Azure OpenAI Service 
Amazon Bedrock 
Google Vertex AI Open-weight models via Hugging Face for tasks where they outperform.
Document AI and Orchestration
LangChain 
LangGraph 
LlamaIndex 
Unstructured Custom extraction pipelines where document complexity warrants. Vector stores: Weaviate, Qdrant, Milvus, pgvector.
Deployment and Integration Platforms
AWS 
Azure 
Google Cloud 
Databricks 
Snowflake On-premise where data residency requires it. Integration with your existing systems of record (Salesforce, ServiceNow, Workday, custom ERP).
Cloud-agnostic by architectural decision, not by inheritance.
Bring the workflow. We return with scope, timeline, and a defensible SOW.
What to Expect from an AI Workflow We Deliver
Production-grade AI workflows typically land in the following ranges. Specific outcome targets are scoped during Discovery and written into the SOW.
40 to 60 percent on document-heavy workflows.
60 to 80 percent depending on document complexity.
aligned to or below the manual baseline within 60 days of cutover.
Where a target falls outside range due to constraints in your estate or workflow, we surface that during Discovery and scope to a defensible number. We do not commit to outcomes we cannot defend.
From the Workflow Practice
A mid-program technical delivery for a Big Four prime contractor. We took over critical modules of a stalled state government welfare platform and shipped them on the contracted timeline.
Most AI agents are prompt layers, not products. Here's why most agent startups won't survive, and the execution architecture that actually lasts.
Golden datasets, regression suites, automated evaluator models, drift detection, and the handoff criteria for moving an LLM from internal use to customer-facing. Written for engineering teams about to ship.
Scope an engagement
Scope a Workflow Engagement
Tell us where you are - building, modernizing, or remediating. Thirty minutes is enough to know if there's a fit and what shape the engagement would take. If we're not the right firm for what you need, we'll point you to who is.