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
AI implementation services for production workflows.
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
What you can hire us to deliver.
Internal AI tools and automation
Build focused tools that help operators finish repeatable work faster, inside the systems where that work already happens.
Agentic-coding rollout
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.
Evaluation and governance
Define the production scorecard and test the behavior that matters, then document the controls and review paths the system runs under.
Portfolio deployment playbooks
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
Keep engineering judgment close to the operating context.
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
A 30/60/90-day path from selection to capability.
Days 0–30
Select and score the workflow
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 and ship
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
Harden and transfer
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
A clear workflow that lacks delivery bandwidth.
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
Who is a good fit?
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.
What happens first?
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.
Do you only build AI agents?
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.
What does production mean here?
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
Tell us which workflow needs to move into production.
Describe the workflow and its systems, plus the operating outcome you want to improve.
Discuss a deployment