Run your AI transformation on one platform, not a pile of pilots.
Most AI programs stall between pilot and production. Every project rebuilds model access, credentials, approvals, and cost tracking, then waits for its own security review, and nobody can say what the whole program costs. Record gives the program one foundation: each new project brings its agent and inherits the rest, with spend visible and controlled from the first call.
Standing up an AI team? Start everyone on the same platform.
Program lead
One inventory of every agent with its owner, what it costs, and whether it is working. Report on the program from the platform, not from spreadsheets.
Builders
Start every project from approved models, tools, and company knowledge. Describe a new agent or bring one your team already built, and keep it working the way it does.
Security and risk
Owners, scoped access, policy, and human approval are part of the platform, so every project runs on controls you have already approved.
Finance
Spend attributed to every team and agent, budgets checked before each call, and a clear view of where routing and private models cut the bill.
Build the foundation once. Reuse it on every project.
11 products in 5 groups. Adopt the whole platform, or start with the block your program needs first and add the rest as it grows.
Connect
One approved way for every project to reach models, tools, and company knowledge.
Run
A place for agents to run, with nothing for each project team to operate.
Control
Access, policy, and approvals that security signs off once for the whole program.
Observe
One view of what every agent costs, who uses it, and where it fails.
Improve
Prove each change before it ships, and cut the cost of repeat work.
Move projects from pilot to production without starting over.
The tenth project should not take as long as the first. Teams describe a new agent or bring the one they built, and it starts with an owner, scoped access, and policy already in place.
Each new project inherits what the last ones put in place.
Model access, the tool catalog, access rules, approvals, and spend tracking are already set up. A new project adds its agent and the access its job needs, not another platform.
Bring the agents your teams already built.
Agents from earlier pilots deploy as they are. Record adds the controls around them instead of asking teams to start over.
Show risk owners what an agent will do before it goes live.
Run a new agent against simulated tools under the same policy it will have in production. Nothing reaches a real system until you decide it should.
Put agents where people already work.
Start the same agent from Slack, Teams, your own apps, a schedule, or another agent, with one set of controls behind every entry point.
Know what every project costs, down to the agent.
AI spend is easy to start and hard to explain. Record attributes every call to the team, agent, user, and model behind it, so the program can answer for its cost before anyone asks.
Show what each part of the program costs.
Model calls, tool calls, and sandbox time are attributed to the team, agent, user, and model behind them, and reconcile to the provider invoice.
Find the agents that cost more than they are used.
See runs, active users, and spend side by side for every agent, so the next funding decision starts from usage, not anecdotes.
Give every project a budget it cannot overrun.
Set caps per agent, team, or key. The gateway checks them before each call, so a project stops at its limit, not after the invoice.
Spend less on every call as the program grows.
Savings come from routing, reuse, and limits, not from asking teams to use AI less. The more work moves onto the platform, the more each one compounds.
Stop paying frontier prices for routine work.
Simple requests go to fast, inexpensive models and hard problems go to frontier models, with estimated savings reported for every request. Identical answers are reused instead of paid for twice.
Catch a runaway agent before the invoice does.
A retry loop at 2 a.m. stops at its budget, and the owner hears about it, instead of finance finding it at month end.
Turn your best work into models that belong to you.
Every approved run is know-how your company paid for. Train it into a private model for the work you repeat most, then route each task between your model and frontier models for high quality at lower cost.
Teach a model with work your team already approved.
Reviewed agent runs and your own datasets become the training set, with personal details removed. You choose exactly what it learns.
A model that belongs to your workspace, never a shared one.
It is trained from an open-weight model and hosted privately behind your gateway. Your examples train your model and no one else’s.
Know it beats your current model on your own cases.
Accuracy, cost per successful task, and speed against today’s model on cases it never saw in training, with break-even shown before you spend.
Route each task to your model or a frontier one.
Add your private model to smart routing beside the frontier models you already use. Routine, high-volume work goes to your model at up to 60% lower cost at scale, and hard problems still go to frontier and reasoning models.
Train only where the volume justifies it.
The estimate shows training, hosting, and the monthly volume where the model pays for itself. When the numbers say wait, it says so.
Get security to yes once, not per project.
Security review is where most pilots wait. On Record the controls belong to the platform: every agent has an owner, access is issued per task, sensitive actions wait for a person, and every action is on record.
Know every agent in the program and who answers for it.
One directory of every agent, its owner, and the person it acts for, so nothing the program deploys goes unaccounted for.
Give agents access for the task, not forever.
Access is issued for one task, used on the agent’s behalf, and expires when the task is done. Agents never hold the credential.
joiner-mover-leaver wants to grant admin in Okta to a new contractor.
Keep a person on every high-stakes step.
Payments, production changes, and sensitive access wait for the right person in Teams or Slack, and every decision stays on record for audit.
Bring your AI roadmap. See what each project inherits.
Map your next projects onto Record: what each one reuses, what it will cost, and what your security team needs to approve.