The control plane for AI.

Everyone's AI runs somewhere.
Yours runs where you decide.
Every decision provable.

What we solve for enterprises

AI value stuck outside your P&L.

Pilots that stall before production - data can't leave, decisions can't be audited, no one owns the model risk. frIdA runs them on infrastructure you control and records every decision, provable to auditors and regulators.

Bring the pilot that never reached production.

Start a five-day Discovery → An engineer, not a rep, replies within 48 hours.
frIdA Control plane org · bank-3
OverviewRoutingEnvironmentsAuditCost
--:--:--Live
TimeWorkloadRule firedModelEnvironmentCostSignature

frIdA control plane, live. Every row is a signed decision: which model, which environment, what it cost, what came back.

The data never left.
The AI went to production.

Each one began with hardware nobody could run, work done by hand, or a week for every answer.

Banking Higher Education Marketing Insurance
Savant, the industry advisor, inside Saptiva Studio

Savant, the industry advisor, inside Saptiva Studio: consumer-book risk across the banking system, answered inside the bank’s perimeter.

Banking

A week per market answer. The data could not leave the bank.

Risk data, regulation and the bank’s own book lived in three places. Every answer meant an analyst, a week, and a spreadsheet no auditor could trace.

Asked once, inside the perimeter, answered with sources attached. Savant, the industry advisor, in production in two weeks.

EnvironmentOn-premises, inside the bank’s perimeter
SourcesPublic banking-system data · Regulation · The bank’s own book
Time to prodTwo weeks
RecordEvery answer signed, sources attached
In productionFrom the record · growing
The AI lab's GPU panel in Saptiva Studio: capacity and running workloads

The lab’s GPU panel in Saptiva Studio: one node, eight GPUs, 759.8 GB of capacity, reserved versus available per card, and the models running on each.

Higher Education

24 GPUs in the building. Nobody to run them.

A new engineering program needed a serious AI lab, on-premises, without hiring a platform team or renting the cloud. The hardware sat idle. Research stayed on laptops.

Saptiva Studio on their cluster, eight funded projects running, faculty trained. Three months from powered-on hardware to researchers in production.

EnvironmentOn-premises. 24 GPUs, 120 virtual desktops, local inference. No data leaves campus.
SourcesSigned project charter (Mar 2026) · Research call, 8 projects approved (Jul 2026)
Time to prodThree months
Record8 projects approved · 9 active researchers · ~100 people trained · 41 more ready to start
In productionYes
Marketing Core: mail-planning results per microzone

Marketing Core, results view: thirty days of mail planning per microzone. Delivered, opened and clicked per campaign, against the prior period.

Marketing

One zone, one email a fortnight. Now 7.5 million users in a single send.

Promos and partners change from one neighbourhood to the next, but every email was built by hand. One zone got one email every two weeks. The rest of the city heard nothing.

Marketing Core picks partners and promos per microzone, generates image and copy, builds the email on the team’s template and publishes it, all on the client’s own infrastructure. One month to the first real send.

EnvironmentThe client’s own cloud, storage and email platform. Image and copy by Saptiva AI workflows.
SourcesThe client’s data warehouse: partners, live promos, new restaurants, microzones. CRM team sheets.
Time to prodOne month
Record3,903 emails across 784 microzones in two months · 51.4 M sent · 93.4% delivered · 17.9% opened · Largest send: 7.5 M users in one day
In productionFrom the record · growing
Dictaminador: the document-ruling workflow in Saptiva Studio

Dictaminador, the ruling workflow in Saptiva Studio: read, filter, validate against the criteria, sign the verdict into the record.

Insurance

Every file ruled by hand. No record of why.

Verification and rulings ran manually: slow, uneven between analysts. When a carrier or an auditor asked why a file was approved, the answer was somebody’s memory.

Dictaminador ships the ruling: documents verified, judged against the insurer’s own criteria, every decision signed into the record. Two weeks to production.

EnvironmentPrivate cloud
SourcesCase files, policies and the insurer’s own ruling criteria
Time to prodTwo weeks
RecordEvery ruling signed into the record
In productionFrom the record · growing

Build in Studio.
frIdA decides where it runs.

Swap the model. Change the cloud. Cross any border. Zero lock-in.

Saptiva Studio · The command center

Build, operate, govern.

Where your organization builds and runs all of it. Savant, RAGster and Dictaminador ship here. In production in weeks.

See what ships in Studio →
ShipsSavant · RAGster · Dictaminador
BuildsAgents & workflows, on your data
GovernsModels & hardware · Cost & audit
Flows Agents Assistant Laboratory Store Models Catalog Hardware
Six workflows in production. State, attention, executions over 30 days.
frIdA · The control plane

Routes every workload. Signs every decision.

Decides where each workload runs and writes a signed record: which model, which environment, what it cost, what came back. Nobody sits at it. It runs whether you are watching or not.

See how frIdA routes →
DecidesModel · Environment · Cost, per request
WritesA signed record for every decision
RunsCloud · On-prem · Air-gapped · Hybrid
Control plane Audit
Every row is a signed decision: model, environment, cost, what came back.
frIdA Control plane org · bank-3
OverviewRoutingEnvironmentsAuditCost
--:--:--Live
TimeWorkloadRule firedModelEnvironmentCostSignature
Saptiva Studio · Audit log

The model will change.
The switch stays yours.

Models are the part that changes. frIdA is the part that does not: where it runs, what it costs, and the proof of every decision stay under your control. Three ways in, one control plane - the same routing, audit and cost at every level.

Prove every decision. Audit-ready by default.

Every routing decision and model output is written to an immutable trail your auditor can read without a data request.

Per recordModel · Environment · Cost · Output · Signature
ChainEach record references the one before it
AccessRead-only view for audit · exportable
ResidencyThe trail lives where the workload ran
frIdAAudit · Record2ef9·1701Verified
Workloaddoc-extraction
ModelRAGster · v2.4
EnvironmentOn-prem · mx-qro-dc1
Routed byRule 01 · request contains personal data
Input3 documents · 41 pages
Output27 fields extracted · 2 flagged for review
Cost$0.0028
Signed2026-09-11 14:02:09 · ed25519
Chain
704d·ddac
2ef9·1701
91c3·0be2
Any edit breaks the chain. Verification runs on every read.
Export recordOpen in SIEM

The risks that stop AI before production.

The obstacles standing between your teams and the value AI can deliver. Every business faces them, and the stakes rise with your obligations. Each one has an owner who has to say yes. The control plane answers all four.

RiskOwnerWithout a control planeWith frIdA
Loss of control over dataCISOPeople and agents send data straight to AI providers with no control point in between, including regulated data you’re accountable for.Data never leaves your jurisdiction. On-premises or air-gapped, inside your perimeter. You hold the keys.
Cost with no ceilingCFOAI spend with no caps and no attribution by user, team, or workload, resulting in unbudgeted operational risk.One governed view of AI cost. Every execution metered, every dollar attributed to a workload and a team.
Shadow AICTONo one can say which AI tools are in use, by whom, or on what data and workflows, creating an un-auditable control gap.Every workload routes through one control plane. Swap models and clouds without rewriting it. Zero lock-in.
No record of AI’s actionsComplianceNo audit trail of what AI did on your behalf when something goes wrong, the tamper-evident evidence regulators now demand.Prove every decision. Every routing decision and model output written to an immutable trail your auditor can read without a data request.
All four · Or no dealEach risk has an owner. Each owner has to say yes.

Your AI runs where you decide.

Same platform, same experience, whichever environment you require. When the rules change, you do not replatform.

How it worksdecided at the moment of action - when a prompt, agent, or tool-call reaches a model
Your people & agents
employees, apps, autonomous agents
promptagenttool-call
frIdA
your decision, signed
Route
Cap spend
Hold
Block
Any model, anywhere
cloud, on-premises, air-gapped, hybrid
DeepSeekLlamaKALyours
Every request written to a signed record as it runs.
AI keeps flowing, under your control, inside your perimeter.

Cloud

Routing here

AWS, Azure, GCP, OCI. Private. Multi-region.

On-premises

Routing here

Your datacenter. Your hardware. Your perimeter.

Air-gapped

Routing here

Zero external connectivity. Sealed.

Hybrid

Routing here

Sensitive workloads inside. Burst to cloud. One control plane across both.

Control is not where your datacenters are. It is who holds the switch.

Your stack, your keys →

Own your AI. Do not rent it.

95%

of enterprise AI pilots never reach the P&L.

MIT
20%

of workloads will move from global to local providers. Every move is a routing decision.

Gartner
2 wks

Savant in production inside the bank's own perimeter.

Invex

You will not buy your AI. You will own it. It runs inside your perimeter, and it keeps running if we disappear.

“Model selection, routing and where inference runs become an ongoing operating discipline, not a one-time choice.”

Deloitte · August 2026

One control plane at the moment AI acts.

Fragmented environments and tools cannot step in when a prompt, agent, or tool-call reaches a model. frIdA does.

AlternativeThe conflict
01Single-jurisdiction cloudsOne control plane per cloud. Outside it, the client starts from zero.
02Model-first platformsTheir model first. Routing to a rival is revenue leaving their model.
03Ontology platformsYour business's meaning moves into their layer. Leaving means extraction.
04Developer gatewaysSold by the seat to engineers. Routing goes to zero. Production does not.
05Incumbent stacks and hoursInside the account already. Sold in hours, delivered in twelve months. Two weeks cuts both.
Saptiva AI

Every environment, every model, every tool.
One control plane, one signed record, two weeks.

The full wall →

What is a control plane?

One layer between everything that calls a model and everywhere a model can run. It decides where each request goes, enforces your rules and signs the record. That layer is frIdA.

frIdA CONTROL PLANE Decides · Enforces · Proves a979·45b9 ✓ signed YOUR ENVIRONMENTS · YOUR MODELS · YOUR DATA
Record

Which model, which environment, what it cost, what came back. Signed.

Environments

Cloud · On-premises · Air-gapped · Hybrid. One control plane across all four.

Routing

Per workload, per request. Your rules, your keys. Nobody sits at it.

Audit

Append-only trail your auditor reads without a data request.

Discovery. Five days. Your data. One provable workflow.

Bring the one thing you cannot get into production. We build it on your data, inside your perimeter, on frIdA. You leave with a signed record of every decision it made.

Bring a workflow →An engineer replies within 48 hours
Day 0

Bring a workflow.

Describe the one thing that will not ship. An engineer, not a rep, replies within 48 hours with what we would need.

Day 1 – 4

Built on your data.

Inside your perimeter, on frIdA. Your model choice, your environment, your keys. Nothing leaves.

Day 5

The decision record.

What ran, where, what it cost, what came back. Signed. Yours to keep, whatever you decide next.

Someone will own the control plane for AI.
It will be us.

Bring a workflow →An engineer replies within 48 hours