Deel
Launched Deel AI Workforce — 7 specialised agents across HR, payroll, talent (Aug 2025).
Same legal-exposure shape as Copilot answering a French overtime question, scaled across 150+ countries.
Source: deel.com/blog
Why this is the moment
AI agent-driven solutions are overtaking the market — and will be the norm in months.
Companies pulling ahead are transforming the processes their teams already run — complex document creation, human workflows, Salesforce analysis, AI context gathering, support triage — into agent-driven loops where one engineer can do what a team did. They are not hiring more people to run yesterday's processes. The risk you are managing is misuse of AI, not whether to use it; the engagement is what keeps your AI surface from shipping the failure modes that come with rushing.
- 3-4 wks first agent loop in production
- 1 dev owns infra → app → agent loop
- 30+ self-hosted services I run today
What your competition is doing
Three peers already shipped LLM-fronted reasoning over legally-binding data.
Each one is the bar PayFit Copilot is now compared against on every renewal call. Same audience, same legal-exposure tail, same need for an eval discipline that defends the prompt change before it ships.
Rippling
Launched Rippling AI — copilot across HR, IT, finance on a unified-data graph (Mar 2026).
Their wedge is the unified-data layer Copilot answers from; Jetlang-as-source-of-truth is your equivalent unbuilt.
Source: rippling.com/blog
Personio
Shipped AI-powered chatbot inside Personio Conversations for SMB HR.
Same SMB-without-in-house-compliance-officer audience as Copilot; the wrong-answer cost is identical.
Source: personio.com/press
The peers all ship LLM-fronted reasoning over legally-binding data. The differentiator is the eval and observability discipline behind every prompt change, not the model.
The threat nobody is pricing in
Is your company ready to compete with the 1-person companies that will emerge in the next 12 months?
A single engineer with the right agent stack will out-iterate most product teams over the next year. The companies that survive are the ones whose know-how is documented as the work happens — so it can be reshaped at speed — and whose internal tools are agent-ready — so a pivot takes days, not quarters. I help you build both, in parallel with the shipping you already have to do.
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Document the know-how
Decisions, runbooks, and tribal knowledge captured by the same agent loops that do the work — not a quarterly Confluence audit.
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Make tooling agent-ready
Internal tools that your AI loop can drive — so the next process you transform is one prompt away, not one rebuild.
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In parallel with shipping
No 6-month transformation programme. The artefacts compound from week one, alongside the roadmap you already have.
Closest match in my portfolio
This one's been shipped — to a stack that overlaps yours.
Production data platform (fintech SaaS) — traditional data engineering, transformed into agent-driven development
Your team runs equivalent process loops in your domain — the shape of the work is the same.
De facto tech lead, 2-3 person team. I took a traditional data-engineering process — schema-change PRs, model authoring, pipeline-job authoring, on-call triage of pipeline failures — and turned it into an agent-driven development lifecycle. Feature/fix cycle compressed from days/weeks to hours/minutes; daily pipeline failures went to near-zero; the team stayed the same size while the throughput tripled. I migrated Redshift→Snowflake mid-flight on the same platform.
- Airflow
- Snowflake
- Terraform
- EKS
- ArgoCD
- dbt
- Langfuse
What I'd do in your first ~90 days
Concrete, not aspirational.
I automate the processes and expensive human loops your team runs every day — complex document creation, human-in-the-loop workflows, Salesforce analysis, AI context gathering for product features, support triage, on-call response — the same way I automated a traditional data-engineering platform. The pattern is repeatable: pick one process where a person's day is full of mechanical stages between trigger and outcome, wrap an agent loop around those stages, ship receipts weekly. The result is the same capability available to your whole company, not gated behind one team's bandwidth.
One short call this week.
If after a 15-minute call it isn't the right fit, no pressure — and you keep the analysis I already wrote.
Fidel Perez · Senior Data Engineer · AI-First · 11+ yrs