Stage 2: Build
The assessment found the work. Now someone has to build it.
Forward-deployed engineers embed with your team, in your systems, and build AI into how the work gets done: software runs the workflow, your people review the exceptions. Business analysts and AI specialists work alongside them, led by a Portfolio Success Partner and a Delivery Lead. The people who design it ship it.
- days to the first production workflow
- 100
- months for a full Build
- 3 to 6
- back-office functions
- 5
- starting investment
- $75K
Azure
Build is the stage that moves your P&L. The Assess readout scopes it; our engineers build it inside your team.
The back-office functions where AI pays back fastest.
The workflows that drive cost, touch every transaction and carry the most manual work.
Finance and accounting
Automated reconciliation, AP and AR processing, anomaly detection, real-time cash flow visibility. The workflows that touch every dollar in the business.
Procurement and supply chain
Vendor selection, demand forecasting, automated PO processing, contract analysis. Lower cost of goods, better working capital.
HR and people operations
Onboarding, benefits administration, compliance document processing. Less administrative load on teams that are already stretched.
IT operations
Ticket routing and resolution, infrastructure monitoring, security event triage. Systems keep running without headcount growing to match.
Customer operations
Case routing, response drafting, SLA monitoring, churn prediction. Better retention, lower cost to serve.
From roadmap to production in 3 to 6 months.
Four steps, one pod, no hand-off between the people who design it and the people who ship it.
- Step 1
Scope and architecture
Define the target workflows, design the architecture, and fit the pod to your team and systems.
- Step 2
Build and integrate
Production agents (software that runs a whole workflow, with a person reviewing exceptions) and automations wired into your ERP, CRM, HRIS and operational systems.
- Step 3
Deploy and measure
Ship to production, set the baseline, and measure the EBITDA impact against it.
- Step 4
Optimize and hand off
Tune performance, train your team, and document the playbook so the next company starts ahead.
A senior team that embeds with yours.
Every Build is staffed with dedicated senior people, not a pyramid of junior associates billing hours while they learn your business.
- Strategy and alignment
Portfolio Success Partner
Your single point of contact across the operating partner, company leadership and the technical team.
- Board and operating review reporting
- Change management and adoption
- Stakeholder communication
- Value creation plan alignment
- Architecture and design
Delivery Lead
The senior architect who owns the technical vision and turns business priorities into production systems.
- Process audit and workflow mapping
- Data model and system design
- Technical quality and governance
- Success metric definition
- Build and ship
Implementors
Forward-deployed engineers who ship with agents, not around them. They write the code, build the automations and integrate with your systems, scaled to the scope.
- Production code and automation
- System integration and testing
- Model and agent deployment
- Claude, Databricks and your enterprise tools
How the pod grows
The engineers own it end to end.
A small pod talks to your experts directly. No extra layer between the people who build it and the people who use it.
Add a product owner.
The more technical the work, the more it needs someone to turn your experts’ judgment into specs and tests. One product owner sits between the engineers and your team.
When the scope is already well defined and you have a change manager in place, the engineers alone are often enough. Change management itself works best as a role inside your company; we support it, you own it.
$75K to $350K
Depending on scope, data readiness and rollout pace.
Milestone billing. You pay for progress, not hours.
Tie a portion of the fee to measurable savings or EBITDA improvement. Our upside is your upside.
- Operations · Level 3500xfaster rewriting of requirements to the INCOSE standardFord rewrites 13,000 engineering requirements in 4 hours instead of 2,170Ford Motor Company
- Healthtech · Level 385%less manual processing time per patient referralElara Caring cut referral-to-admission time from days to under 24 hours
- Private Equity · Level 37xmore companies researched each weekBregal Sagemount screens 7x more companies a week with LLM deal sourcing
Frequently asked questions
A 3 to 6 month engagement where forward-deployed engineers embed with your company to ready the data, then architect, build, deploy and measure AI in your operations.
Finance and accounting, procurement and supply chain, HR and people operations, IT operations, and customer operations: where mid-market companies see the fastest payback.
An engineer who works inside your company, next to the people who do the work, and builds the system there. The profile changes with the level: at L2 they sit close to your subject matter experts; at L3 and L4 they lean toward data and back-end engineering. At every level they start from the output and the test it has to pass.
No. Where a step needs the same answer every time, plain code runs it: a rule, a decision tree, a script. Agents handle the judgment between systems. Where a system you already own or are buying will do the job, we use it instead of building around it.
$75K to $350K per engagement, depending on scope, data readiness and rollout pace. Milestone billing: you pay for progress, not hours, with optional outcome bonuses.