Sovereign
Dynamic Sovereign Routing
Federis checks who is asking, then sends the request only to GPU capacity and document stores inside your boundary, chosen by data classification and location. Nothing is dispatched outside it.
[ Core features ]
Six features
Every request between your applications and your models passes through six controls, all enforced inside your own boundary. Together, they spell the safest way to run enterprise AI.
Sovereign · Adapters · Filtering · Execution · Shield · Trail
Adapters
Decoupled Pluggable Adapters
Databases, identity providers and vector stores connect through defined interfaces, so each one can be replaced without touching the core. It also keeps the core free of restrictive open-source licences.
Filtering
Pre-Guardrail Filtering
Personal identifiers such as Aadhaar and PAN numbers are masked, and prompt-injection attempts are stopped, before a request reaches a GPU. Unsafe or toxic content is filtered from the response on the way back.
Execution
High-Performance Serving Gate
Requests wait in managed queues, and capacity scales with demand. A model is admitted only after its catalog entry validates, and if a target fails, circuit breakers shift traffic to a fallback.
Shield
Explainable Hallucination Shield
Each answer is broken into individual statements, and every statement is traced to a specific passage in your own documents. Answers that are not supported strongly enough are blocked before they reach the user.
Trail
Tamper-Evident Ledger Vault
Every decision is recorded in a chain of SHA-256 hashes, so any later change to a record can be detected. The chain is exported to write-once storage that cannot be overwritten.
SovereignAdaptersFilteringExecutionShieldTrail
Built on open standards
The control plane is assembled from standard identity, policy and transport building blocks, so security teams and auditors can inspect each one.
- Identity
-
Open standard identity
OIDC and OAuth2 for identity and access.
- Policy
-
Declarative access policies
Sovereign access policies written as RBAC and ABAC rules.
- Workloads
-
Cryptographic workload identity
SVIDs and mTLS verify every workload.
- Retrieval
-
Restricted vector search
Metadata-restricted vector search datastores.
- Inference
-
Inference gateways
High-performance inference execution gateways.
How every request is handled
Every request passes five checks
Each check is enforced by policy and recorded for audit, before an answer leaves the system.
- 01
Identify
Know who is asking before anything runs.
- 02
Decide
Policy decides, not the model.
- 03
Guard
Check the prompt and the evidence before inference.
- 04
Serve
Run it inside your boundary.
- 05
Prove
Every decision leaves evidence.
Enterprise adoption risk
Why enterprise GenAI programs stall
Successful adoption depends on operating discipline: controlled spend, governed data access, evidence quality, deterministic policy and clear human accountability.
No measurable return
95%
of enterprise generative AI pilots delivered no measurable profit or productivity impact. Most programs never connect model usage to business value.
MIT NANDA, The GenAI Divide, 2025
Initiatives abandoned
42%
of companies abandoned most of their AI initiatives. Pilots stop when integration complexity, unclear ownership and production cost arrive.
S&P Global Market Intelligence, 2025
Projects forecast to be abandoned
60%
of AI projects not supported by AI-ready data are forecast to be abandoned through 2026. The blockers are weak data, fragmented systems and insufficient governance.
Gartner, February 2025Six failures behind the numbers
Runaway token economics
What goes wrongAgentic workflows turn AI spend into a variable, opaque cost centre. More usage is often mistaken for more business value.
How Federis answers it
Model routing, token-budget governance, approved runtime lanes and local or open-weight execution keep usage controlled by policy and workload value, with an audit trail behind it.
Automating bad processes faster
What goes wrongPilots automate broken workflows instead of redesigning the decision path around data, controls, exceptions and accountability.
How Federis answers it
Every workflow passes through identity, policy, evidence, guardrail, approval and audit checkpoints, so GenAI becomes a controlled process layer rather than a chatbot shortcut.
Customer-facing autonomy without escalation
What goes wrongFully autonomous bots collapse at the edge of nuance, empathy, refunds, complaints and regulated decisions.
How Federis answers it
Bounded automation with human approvals, safe refusal, evidence-confidence checks and role-based escalation before a sensitive output or action is released.
Guardrail tax and weak integration
What goes wrongEnterprises underestimate the cost of safety controls, data readiness, retrieval quality, logging, model governance and system integration.
How Federis answers it
These controls are the product core: guardrails, retrieval fabric, model governance, sovereignty, observability and append-only audit are native control-plane behaviours.
Pure LLMs where deterministic logic is required
What goes wrongRegulated operations need explainability, latency guarantees and rule compliance that a general-purpose LLM cannot provide alone.
How Federis answers it
Hybrid AI: models extract and summarise, while OPA policies, rules, permissions, evidence thresholds and approvals govern the final decision.
Data sovereignty and cloud dependency
What goes wrongSending sensitive prompts, customer records or operational telemetry to public APIs creates compliance, latency, procurement and dependency risk.
How Federis answers it
A vendor-neutral control plane that runs on premises, in a private cloud, air-gapped or on GPU-backed sovereign infrastructure, with replaceable adapters.
The pattern that works
- Small, specialised models for high-volume, repeatable tasks.
- Local or sovereign GPU lanes where data residency, latency and cost matter.
- Hybrid deterministic policy and rule gates for regulated decisions.
- Evidence-grounded outputs, human approval paths and tamper-evident audit.
- Pluggable infrastructure, so the enterprise is not locked to one model vendor or cloud.
Two products, one governed platform
Control the AI. Then build on it.
Federis governs how models are reached and used. Federis Studio is where teams build agents and workflows on top of that governed layer.
The control plane
Federis
A modular sovereign control plane, network controller and security proxy. It sits between applications and GPU-backed LLM serving layers and separates compute and storage from central governance and audit.
- Zero-trust identity and access enforcement
- Engine-based routing by geofencing and classification
- Pre- and post-guardrail verification
- Tamper-evident ledger archiving
The studio
Federis Studio
A visual canvas for agents and workflows. A Copilot drafts a workflow from a plain request, and risky actions wait for human approval. On Federis, every run uses deployed models, policy, guardrails, retrieval and audit.
- Visual workflow canvas and templates
- Multi-agent runtime with durable runs and replay
- Human approvals for risky tool calls
- Standalone or platform edition
From infrastructure to sovereign intelligence
The unified control stack puts policy, identity, serving and audit between your applications and your physical GPUs, so governance does not depend on which hardware or model sits underneath.
Where it runs
Built for the sectors that cannot afford a leak
Guardrail boundaries structured for defence, financial services, government, energy and critical infrastructure, where data residency, accountability and audit are not optional.
Air-gapped command and control safeguards
The challengeDefence and national security teams cannot leak intelligence to public networks, and cannot let command decisions be shaped by unsupported AI output.
How Federis helpsGeofenced deployment lanes with outbound controls, prompt-injection mitigation, and a hallucination shield that checks answers against approved briefs and refuses safely when grounding confidence falls below the configured threshold.
- Classified air-gaps
- SPIFFE workload trust
- Zero egress boundary
Customer data protection and regulator-ready records
The challengeBanks and financial institutions face strict penalties for exposing customer PII such as Aadhaar numbers or card details, and must keep immutable records of every AI-assisted decision.
How Federis helpsPre-guardrails detect and mask PII inside the security boundary. The audit vault chains every step, from user clearance to the OPA decision and model parameters, with SHA-256 hashes and archives it to WORM storage.
- RBI digital guidelines
- DPDP Act redaction
- WORM audit
Information sovereignty and administrative gating
The challengeState departments need access controls that separate public distribution from internal classified briefings, with strict compliance tracking.
How Federis helpsRBAC and classification ceilings are enforced through OPA. An analyst queries department data within region exclusions, and a public-facing feed passes content-safety guardrails before publication, with a complete administrative audit trail.
- RBAC clearance gating
- OPA policy gating
- Administrative accountability
Guarded AI for high-impact grid operations
The challengePower and energy operators run environments where decisions affect public safety and grid stability. An AI assistant there must be rigorously constrained.
How Federis helpsControl-room assistants are grounded in approved technical manuals. Unsafe or unsupported procedures are rejected before they reach an operator, and every query is recorded for incident review.
- Approved-manual grounding
- Fail-closed refusal
- Tamper-evident records
Secure AI for public services and utilities
The challengeRailways, telecom, ports and emergency services need enterprise-grade generative AI that does not depend on a third-party cloud.
How Federis helpsTenant isolation, geofenced routing and least-privilege access across operators, with a transparent audit trail for every operation, on local and redundant GPU lanes.
- Tenant isolation
- Geofenced routing
- WORM audit vault
Security and trust
Security is product behaviour, not an afterthought
Access boundaries, operational evidence and component licensing have to survive scrutiny from security teams, auditors and procurement. Each of these is built into the control plane.
Zero-trust identity
Users and workloads are verified before they reach a model. Keycloak handles user identity, SPIFFE and SPIRE issue workload identities, and mTLS secures every service-to-service call.
Fail closed
If an identity, policy or guardrail decision cannot be completed, the request is denied or flagged for manual review. It is never allowed by default.
Tamper-evident audit
Each request, policy decision and response trace is chained with SHA-256 hashes and archived to WORM storage, so a record cannot be altered after the fact.
Air-gap ready
Runs fully on premises and offline, with no dependency on a public cloud. Every feature works inside a restricted network.
Permissive licence discipline
The core uses Apache 2.0, MIT and BSD components. Copyleft products such as AGPL run only as separate services behind adapters, never inside the core.
Replaceable integrations
Policy, identity, retrieval, inference and storage are reached through adapters, so no single component locks you to one vendor.
Sovereign economics
At sustained volume, owning the lane costs less
Public API spend grows with every token. This modelled workload is a mid-tier bank processing 1 trillion tokens a month. After the first year, an owned sovereign lane runs at a fraction of managed API cost.
Owned sovereign lane, year 1
₹30.22 Cr
Hardware, operations team, Federis licence and implementation.
Owned sovereign lane, year 2 onward
₹9.24 Cr
Recurring cost a year, up to 92% lower than managed API.
Gemini Enterprise API, per year
₹87.86 Cr
Negotiated enterprise-volume pricing under the same workload.
OpenAI Enterprise API, per year
₹110.82 Cr
Negotiated enterprise-volume pricing under the same workload.
Modelled with 70% input and 30% output tokens, 80% fast-tier and 20% flagship routing, and an N+1 high-availability pod. Figures are directional: refresh them with current vendor rates and your own costs during procurement.
See Federis in action
Simplify regulatory compliance and zero-trust delivery for your sovereign AI workloads today. Talk to us about your environment and we will walk through the control plane and Studio against it.
- Zero-trust control plane
- Federis Studio on top
- Air-gapped ready