Production agents and workflows
Move a defined business process from manual or pilot work into an operating workflow. We wire in retrieval and tool use with a human-review path, and we keep evaluation and monitoring on it once it runs.
AI implementation services
We deploy reliable agents and internal tools, and we automate workflows around them. Evaluation and governance come built into the engagement, and so does operator training.
01 Business workflow
A named owner and a bounded task
02 Data + systems
Permissioned access and context
03 Agent or internal tool
Bounded production action
04 Human review
Escalation and judgment
05 Evaluation + monitoring
Quality and risk signals
06 Operating outcome
Adoption and ownership
The model and its interface are a small slice of the system. A production workflow needs operating context and review paths around it, with evaluation running and a clear owner in place.
Services
Build focused tools that help operators finish repeatable work faster, inside the systems where that work already happens.
Help engineering teams adopt tools like Claude Code and Codex. Workflows and governance get fitted to their codebase, and evaluation shows whether the rollout is working.
Define the production scorecard and test the behavior that matters, then document the controls and review paths the system runs under.
Help private-equity teams find repeatable opportunities and sequence deployments across the portfolio, so lessons from one company carry to the next.
Why the model works
Workflow ambiguity and data access block AI value, and so do unclear evaluation and the risk that nobody adopts the result. Forward-deployed engineering compresses the loop between business context and software delivery, so the system holds up in the environment where it runs.
Private-equity operating teams have a dedicated AI for private equity model to review. If you lead engineering and are planning coding-agent adoption, Claude Code training for teams covers that rollout.
How a deployment works
Days 0–30
Map the operating process and audit the data and systems behind it. This phase surfaces the constraints and ends with a production scorecard.
- — Workflow and systems map
- — Opportunity and risk assessment
- — Production scorecard
- — Delivery plan and decision gate
Days 31–60
Build the first production workflow and connect the data and tools it needs. Evaluation and monitoring go in before anyone depends on the output, and a human reviews the cases that call for judgment.
- — Working agent, internal tool, or automation
- — Required data and system connections
- — Initial evaluation coverage
- — Operating documentation
Days 61–90
Roll the workflow out and make it reliable under real load. We train the operators, then hand the deployment over as a capability the team can repeat on its own.
- — Reliability and governance controls
- — Operator training
- — Ownership handoff
- — Expansion roadmap
Best fit
This model is strongest for PE firms and portfolio companies, mid-market operators, and enterprise teams that have a clear workflow and need applied-AI engineering bandwidth to ship a production system quickly.
Common questions
The best fit is a named workflow with an accountable owner, backed by a sponsor who can decide on rollout and by data and systems we can reach. A broad mandate to add AI, with no specific workflow attached, is where engagements stall.
We start by mapping the workflow and auditing the data and systems around it. That first phase surfaces the constraints and ends with a production scorecard, before anyone commits to a broader build.
No. What we build depends on the workflow. One engagement might produce an internal tool and a set of automations, another an agentic-coding rollout with the governance to match. Operator training and portfolio playbooks are in scope too, and so is standalone evaluation work.
Production means real people rely on the system inside an operating workflow. Someone owns it. Evaluation and monitoring stay on, and there is a human-review path wherever the work calls for one.
The podcast and field notes document how these deployments go in practice. All episodes
Next step
Describe the workflow and its systems, plus the operating outcome you want to improve.