Most consultancies tell you what they do. We’ll tell you how we do it.
Agents give our engineers more hands — not less heart — at any stage of your project.
Platform-enabled service, and why they work better together
Crunchloop is a platform-enabled service. Dap is the platform we built — and we’re the engineers who deploy it inside your team. Paired, they make each other better: engineers who know how to run Dap on your systems get more out of what it can do, and Dap gives their work somewhere to compound instead of resetting when an engagement ends. That’s the offering we build toward.
OpenAI backed a multi-billion-dollar bet that deployment — not model quality — is the hard part of enterprise AI. What they’re industrializing as the “Forward Deployed Engineer” model is structurally how Crunchloop has operated for years.
The difference: an FDE is a service you buy repeatedly. Dap is a system that stays — governed continuously by Crunchloop, even while agents run unattended.
Why not just running AI tools yourselves:
The default
Every developer uses AI differently. No one knows how much of what ships was AI-generated, or whether it’s safe.
Context lives in your engineers’ heads and scattered docs — nothing feeds it into the next session automatically.
The tool assumes your codebase is already agent-ready. Most aren’t.
Crunchloop + Dap
Every session logged, every PR scored, every policy enforced — visible to you, not buried in the tool.
Context compounds. Every session, every engagement, makes the next one sharper.
We make the architecture agent-grade first — then Dap governs from there.
Why not just hiring a consultancy:
The default
More AI usage means more humans attending every generation step — attention becomes the limit.
Every consultancy brings its own way of using AI — inconsistent, ad hoc, hard to govern across a team.
A consultancy’s AI usage produces more output for this engagement — and nothing that makes the next one easier.
Crunchloop + Dap
Dap unleashes unattended, parallel sessions — governed by policy, not gated by who’s watching.
Crunchloop and Dap standardize how AI runs across your team — one set of policies, one audit trail, not a patchwork of habits.
Dap compounds what it learns. Every session sharpens the context available for what you build next.
See what this looks like for real clients.
Check our WorkWhat clients say
Who does it — the people
Crunchloop’s engineers work forward deployed: embedded in your codebase, running agents, orchestrating sessions, and conducting the platform and the work itself — directing what runs, catching what shouldn’t ship, making the judgment calls that decide whether AI compounds or stalls.
None of this is theoretical. The guardrails in Dap — the agent guards, the context architecture, the review gates — came from years of running agents on real production systems, not a roadmap written in the abstract. We hit the edges ourselves first.
That experience predates Dap by a long way
See Company
Most engagements run with Dap in the loop, but not all of them need to. Clients in a regulated environment, or not yet ready to bring AI into delivery, can still engage the same engineers without the platform. The people are the constant either way.
What makes it last — the platform
Dap is what makes the people’s work repeatable, not heroic — every session logged, every policy enforced, every engagement making the next one sharper.
Other platforms handle pieces of this: code review, agent orchestration, CI automation. Dap is where all of it runs together, governed as one system.
We take data handling seriously, and it shows in how we work: sandboxed sessions, scored merges before anything ships, a full trail of what changed and why.
Security FeaturesWhere it runs is your call: hosted by Crunchloop as a managed service, or deployed into your own infrastructure. Either way, it’s Crunchloop engineers running it — not a hand-off with no one on the other end.
Your platform is the strategic prerequisite for unlocking the organizational value of AI. It is the engine that will turn your AI investments into a true competitive advantage.
Different needs call for different formats.
AI changed what teams need — not just who does the work. What that actually looks like isn’t always obvious yet, even to the people who need it most.
We’re rebuilding the model around what teams need now — not what they needed before.