Why this is the moment
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.
What your competition is doing
Three peers Perk competes with on the controller / CFO buyer shipped agentic AI in production over the last 12 months — each one resolves end-to-end exception traffic, not just flags it. Same shape, different go-to-market.
Navan
Shipped Navan Cognition (Jun 2025) — agentic platform behind Ava in production.
Direct T&E competitor; Cognition fronts their disruption desk handling thousands of chats per day. Perk's AI today flags; Navan's resolves with receipts — that's the renewal-call axis.
Source: navan.com/about/press
Brex
Shipped 'Agents on Brex' in Fall Release 2025 — AI for policy enforcement.
Brex sits beside the same controller / CFO buyer Perk is selling to. 'Agents on Brex' resolves policy exceptions with receipts, not just flags it; the renewal-call axis is the same on the spend side.
Source: brex.com/product-announcements
Ramp
Shipped 'Agents for Controllers' (Jul 2025) — autonomous spend-policy enforcement.
Ramp is the spend-side rival; Agents for Controllers ships with 99% expense-approval accuracy. Renewal-call axis is now resolve-with-receipts vs. flag-and-handoff.
Source: prnewswire.com (Ramp release)
Perk's AI today flags; the renewal-call axis over the next twelve months is whether Perk resolves with receipts the way Navan / Brex / Ramp now do, or stays at flag-and-handoff.
The threat nobody is pricing in
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.
Document the know-how
Decisions, runbooks, and tribal knowledge captured by the same agent loops that do the work — not a quarterly Confluence audit.
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.
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
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.
What I'd do in your first ~90 days
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.
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