Bespoke
agents.
Most agent platforms ask you to fit them. We fit ours to you — your business, your goals, your needs, your APIs, policies, perimeter, budget.
The platform,
in motion.
A short look at how Canall thinks about private agent platforms: deployment where the data already lives, observability on every run, and evaluation loops built around the work your team actually trusts.
I started Canall.ai to solve one problem I had lived — integrations that dragged so long the SaaS deal nearly didn't matter. I built a platform for that. While I was building, I realized the platform itself and the ease with which it was customized was the value prop: a way to deploy agents that doesn't ask you to hand over your data, your costs, or your runway to anyone else's roadmap. So we rallied a team around it.
This is the pivot. Canall.ai is a bespoke agentcy. We built the platform. We forward-deploy it to you. You keep the keys.
The platform is the core — bespoke agents, forward-deployed, yours to keep. But the work around it is ours too: which vendor to choose when it isn’t us, how to run a bid across a dozen subcontractors, how to put a coding agent in two hundred hands. We can give that counsel because we build the core ourselves.
A tailor who only sells you his own cloth isn’t giving you advice. He’s giving you a pitch.
What you would need to build.
the Capabilities we have built
Multi-model abstraction
Any LLM, Any provider
Cost & loop guardrails
Budgets enforced before tokens spend
Secrets management
Secrets broker — the agent never sees the key
Observability & tracing
End-to-end traces, span-level cost & latency
Evaluation harness
Evals + tuning arena, built in
Policy & governance
Policy-based governance at the runtime
Deployment portability
Runs anywhere. Any cloud, On-prem, or Air-gapped
Skill / connector library
Snap-in skills, growing quickly with each engagement
Human-in-the-loop surfaces
Approval surfaces, first-class
Sandboxed execution
Per-session isolation, controlled egress
Agent design & deployment loop
Fluent design, configuration, deploy
A team to maintain all of the above
One platform. A small team. Forward-deployed to you
Hover any row for the long answer.
12 / 12 — built, not announced.
The parts are the easy half.
here is what we cut from them.
Twelve capabilities are just cloth until they’re a finished thing. A few patterns we’ve cut — most representative, one in deployment now. We don’t publish client names: for a house built on keeping data inside the perimeter, discretion is rather the point.
The supplier survey, filled from the record
The chore.You’re surveying a supplier on a paying client’s behalf. The supplier won’t fill it in. They point at what they’ve already published — a sustainability PDF, a page on their site — and say it’s all in there. So someone reads both and copies the answers across, by hand, one question at a time.
The cut.The agent reads what the supplier published, takes each survey question in turn, finds the passage that answers it, and drafts the answer with a citation back to the source line. Where the published record doesn’t actually answer the question, it says so — and hands you a short list of what’s left to chase. No confident guesses.
What stays inside.The survey, the supplier’s documents, and every drafted answer stay inside the perimeter. Each field carries its source, so the finished survey is auditable, line by line — which is the whole point when you’re filling it on someone else’s behalf.
The solicitation, answered from your own record
The chore.A solicitation lands. Sixty pages. Someone reads all of it, pulls every shall and must into a spreadsheet, and starts writing the same past-performance answers for the fifth time.
The cut.The agent reads the solicitation, lifts every requirement into a compliance matrix — requirement, owner, status — and drafts the first pass from your past-performance library, each answer cited. The requirements you have no evidence for, it flags. Those are the ones worth your morning.
What stays inside.Your past performance, your pricing, your win themes never leave the perimeter. The matrix and the draft are yours from the first keystroke.
The answer that lives in one person’s head
The chore.The answer is in a 200-page SOP, a closed channel, and one senior engineer who gets interrupted forty times a day. The knowledge is real. It just doesn’t scale past the one person holding it.
The cut.The agent indexes the SOPs, the manuals, the old tickets — and answers in plain language, with a citation. When it doesn’t know, it tells you who to ask instead of inventing something plausible. The gaps it keeps hitting become the documentation you didn’t know you were missing.
What stays inside.A sealed or local model runs this with zero egress — nothing indexed, nothing asked, nothing answered leaves your walls. The one place this works is the one place your data already lives.
The pile that only scales by hiring
The chore.Dense, inconsistent documents arrive by the hundred. Someone reads each one, lifts the facts that matter, and keys them into the several legacy systems that each hold a piece of the record. High volume, high stakes — and the only way to do more of it has ever been to hire more people to do more of it.
The cut.The agent reads the documents, extracts the fields, and reconciles them against the systems already holding fragments of the truth — writing one clean record and flagging where the systems disagree instead of quietly picking a winner. A person signs it before it lands.
What stays inside.Air-gapped, end to end. The documents, the extracted fields, and every system credential stay behind the wall. Nothing is sent anywhere to be read — which is the only reason data this sensitive can be automated at all.
Tailored to the
seam, not the size.
Every engagement starts with measurements: your APIs, your data shapes, your policy posture, your renewal calendar. The cloth is the same; the cut is yours.
Your data stays where you say.
The platform installs inside your perimeter. The agent runs inside that perimeter. The keys live in a broker the agent never sees. Egress is a list you approve, not a default we set.
Your costs stay yours.
Hard ceilings on tokens, depth, and time — per agent, per task, per tenant. No mystery bills. No 2am loops. No renewal-surprise economics. You can own your LLM server, completely predict the costs.
Your roadmap stays free.
No model lock-in. No framework lock-in. Skills, evals, and policy travel between providers. If a better model lands next quarter, you move. We help.
One platform.
Four deployment shapes.
Pick the perimeter that matches your risk model. The agent doesn't care. Neither will your auditor.
The platform runs in a tenant you own. Data, models, and tools never leave your VPC unless you choose to call out.
Four fittings.
One platform, made to fit you
Some work we cut ourselves.
some we help you commission elsewhere.
The four phases you just read — Discovery, Tailoring, Forward-deploy, Operation — are one commission, start to finish. This is the book they’re drawn from.
The platform is the core — bespoke agents, forward-deployed, yours. The counsel is the work around it: choosing vendors that aren’t us, running bids across partners we don’t employ, planning rollouts on stacks we don’t own. We give that counsel because we build the core ourselves. The thirty-minute fitting is free; everything below is priced so you know what you’re buying before we measure.
The front door to all of it. We measure your integration surface, your data shapes, your policy posture, and the patterns worth cutting — then hand you a written read and an honest call: cut something bespoke, buy off the rack, or some of each. With us or without us.
The main engagement. We tailor the agents to your domain, wire the skills to your APIs, write policy against your perimeter, and forward-deploy it where you said — Hosted, VPC, On-prem, or Air-gapped. You keep the keys.
A brittle workflow you’ve outgrown, rebuilt — and wired so an agent can drive it. Not a migration for its own sake; a migration that ends somewhere an agent can stand.
Telemetry on every run, evals on every change, and a standing line back to the bench when something needs altering. The platform is yours; this keeps it fitting.
Not every problem is a garment we cut — and not every client picks our cloth. Sometimes the right move is a tool we’d never sell you, a partner we don’t employ, or a rollout of something you already bought. We work that ground too. Choose our platform or don’t; the program still needs running, and the counsel is worth taking because we ship at the frontier ourselves.
You’re choosing between frontier vendors and agent tools — Anthropic, OpenAI, Cognition, the one that launches Tuesday. We run the evaluation the way we’d run our own: your real tasks, your real constraints, a scorecard you can take to a board. Our platform is usually in that field, and we’ll make its case where it’s strong and call it where it isn’t. If the cut that fits is one we’d never sell you, you’ll hear that just as plainly.
A bid or a build bigger than any one shop — primes, subs, specialists, all pulling at once. We’re the single mind across the many hands: the response coordinated, the work parceled and sequenced, the seams between partners owned by someone whose name is on the outcome.
You’ve made the enterprise call — a coding agent, a coworking agent, an SDK. Now it has to reach hundreds of people who didn’t pick it. We plan the rollout, build the purpose-built agents on the stack you chose, and stand up the enablement so adoption is a curve, not a memo.
Built for the perimeter.
Ready for procurement.
Canall.ai, Inc. forward-deploys private agent platforms for teams that need AI to operate inside real security, policy, and procurement constraints.
For government proposals, teaming conversations, and contracting packets, our current capability statement is available as a PDF. It covers the work we perform, the deployment shapes we support, and the controls we build around private agent systems.
Why we pivoted,
in our own words.
The founder's letter explains the why. The technical notes show how we turn that posture into systems you can inspect and improve.
Companies are wary of where their data is really going when they use AI.
The Frontier deals are clean on paper and murky in practice. The SaaS platforms wire in AI features hastily and quietly. Provenance gets vague. The agentcy answer is to install the platform where your data already lives — and let the agent come to it, not the other way around.
The platform's job is to make agent budget overruns impossible by construction: budgets enforced before the call, depth and time bounded, every run traced. Cost is a design surface, not a renewal surprise.
Business leaders read about large bills from agents run amok, burning expensive tokens, stuck in a loop.
If you adopt their agentic platform, you are adopting their LLM.
That isn't a partnership; it's a tether. A bespoke agentcy keeps the agent above the model — swappable, portable, evaluable. The platform you deploy this quarter still works the quarter the leaderboard reshuffles.
That is the pivot. The integration use-case is still where we start best. But the same platform — orchestration, governance, evals, secrets broker, deployment optionality — is the answer for any team that needs agents and refuses to hand over their independence to get them.
I built Canall.ai to solve one type of problem. I quickly realized the platform could solve so many more.
Thirty minutes. We talk about your integration surface, your policy posture, your renewal calendar. You leave with a candid read on whether a bespoke agentcy is the right answer — and if it isn't, we'll say so.