Pillar01
Agent Studio
Build, configure, test, publish, embed and observe your own governed AI agents — without writing code. Capabilities, tools, guardrails, and the surfaces they answer on are all controlled through one Studio your team owns.
The Reliability Operating Model · For Regulated Finance
Build · Deploy · Sustain · Govern your AI workforce
Models that get betterevery day. Automatically.
You modelled the PD in two weeks. The MRD, validator pack, drift triage and retraining cycle took two years. Sovaryn runs the model lifecycle as a Reliability Operating Model — a workforce of governed agents that builds, deploys, sustains and governs every model. Steward AI continuously scores your portfolio across six dimensions and auto-opens a Champion-Challenger pipeline whenever a model starts decaying. Your team ships the next model, the platform stewards the last one.
The Problem Section
In a digital lender, the modeller is also the validator. That is the bottleneck.
An NBFC or a fintech lender does not have a separate model-validation desk. The same data scientist who built the new-to-credit scorecard on Account Aggregator and UPI flow data is also the one writing its policy memo for the RBI inspector, signing the IndAS 109 staging logic, defending the fairness audit, and answering the auditor’s e-mail about why the champion model is still on a sample from before the last repo-rate cycle.3
Meanwhile the data underneath her is moving. UPI flow features re-distribute on a quarterly cycle. Bureau pulls lag by a week. The Account Aggregator stack added two new FIPs last month and broke a feature contract no one had registered. A PSI > 0.2 on the top three features now fires every eleven weeks — and there is no one whose full-time job is to catch it.
“I have nine people. Four are building the BNPL scorecard, three are firefighting the collections model the auditors flagged last quarter, and two are writing policy memos. I do not have a validator. I am the validator. The next RBI inspection is in March.”
— Head of Credit Risk, top-10 digital NBFC, Bengaluru
Sovaryn is built for the team that cannot afford a second desk. A governed agent workforce sits next to the data scientist, drafts the policy memo from the actual recipe, watches the PSI feed on the alt-data stack, opens the Champion-Challenger pipeline the day drift breaches the band, and assembles the regulator pack while the modeller goes on building. Same evidence trail for the CRO, the auditor and the RBI inspector. Same trail for the data scientist defending her own work.
3 Sovaryn pilot data, anonymised across four digital-NBFC and two fintech-lender tenants under RBI MD-ITG and RBI Digital Lending Guidelines, FY24. Methodology available on request via Compliance Desk.
The Platform
An operating model, not a workflow.
Sovaryn is not another GRC tool, and it is not a chat assistant. It is a workforce of governed AI agents that runs the model-lifecycle itself — from the moment a charter is registered to the day a regulator asks why the model declined on 14 March.
Sixteen agents ship out of the box. A no-code Studio lets your team build the rest. Every agent runs through one Cascade — versioned, gated, human-checkpointed — so the speed of AI never outruns the speed of audit.
“The first AI platform built around the validator’s decision, not the data scientist’s notebook.”
— Internal product principle, Sovaryn Engineering
Pillar02
Agent Library
Sixteen pre-built agents shipped on day one: MRD authoring, drift triage, validator pack, fairness audit, override review, deployment readiness, regulator response, shadow-AI discovery and more. Clone, configure, deploy.
Pillar03
Cascade Runtime
Every agent runs through Sovaryn’s deterministic seven-stage cascade — versioned, gated, human-checkpointed. There is no “skip” button, even for the administrator. Every artefact carries the previous stage’s real output.
Pillar04
Audit Spine
Every agent action is grounded in a real artefact, signed by a real reviewer, and replayable as one expiring URL. Cited to SR 11-7, RBI MD-ITG, EU AI Act, DPDP. Maker-checker enforced. Overrides permanent.
The Workforce
Sixteen agents on day one. A studio for the rest.
Every Sovaryn agent ships pre-wired to the cascade — versioned, gated, replayable. Configure one in minutes. Build your own in an afternoon.
The Agent Library
— twelve of sixteen shown —
Documentation01
MRD Author
Auto-drafts the Model Risk Document from real artefacts. Cited to the home regime.
Validation02
Validator
Assembles the decision pack — stability, fairness, leakage, lineage, override history.
Monitoring03
Drift Forensics
Triages drift in 90 seconds; commits to one of four typed RCA verdicts with owners.
Compliance04
Fairness Audit
Runs the protected-group panel; emits a number, not a recommendation. Threshold gated.
Governance05
Override Reviewer
Inspects manual overrides daily; surfaces concentration, repeat-reviewer and policy drift.
Release06
Deployment Readiness
Pre-checks all stage-gates before promotion. Fail-closed by default.
Defence07
Regulator Response
Compiles a replayable evidence pack for any past timestamp; emits one expiring URL.
Discovery08
Shadow-AI Discovery
Scans pipelines for unregistered models, retroactively governs them and assigns owners.
Validation09
Backtest Runner
Re-runs historical decisions on revised features; quantifies the swing in approval/loss.
Risk10
Risk Memo Author
Drafts a CRO-ready memo from monitoring deltas and override flags — every fact cited.
Platform11
Cascade Orchestrator
Runs the seven-stage pipeline end-to-end; gates by floor, regime and reviewer.
Operations12
Monitoring Triage
Clusters alerts, dedupes by root cause, routes to the right desk — at 2 a.m. or 2 p.m.
Agent Studio
Build your own agents.
No code. No exceptions.
Studio is where your team turns a recurring operations problem into a governed agent — a fairness audit for credit cards, a backtest for IRB swings, a memo from this week’s overrides. Pick capabilities. Set guardrails. Approve. Ship.
Every Studio agent inherits the same audit spine as the pre-built library: cited artefacts, maker-checker approvals, replayable runs, signed share-links. Nothing escapes the cascade.
Step01
Start from blank or a template
Choose from sixteen pre-built templates — MRD, validator, drift, fairness — or open a blank canvas.
Step02
Pick capabilities and tools
Compose from a governed allowlist: SHAP, fairness panels, RCA verdicts, ticketing, governance comments, owner notifications.
Step03
Set guardrails and triggers
Define floor thresholds, RBAC scopes, cron schedules and signed webhook bindings — all in plain forms.
Step04
Test, approve, embed
Multi-turn chat to test the agent. A human reviewer approves. Embed into any Sovaryn surface or your own.
The Reliability Operating Model
Page A 4
What changes when every modelchases 100.
CONTINUOUS REVIEW
Quarterly reviews become a daily readout.
The Steward reads every live signal — drift, fairness, override, lineage, contract, cascade — and emits a six-dimensional Reliability Score the CRO can read in ten seconds. Every score is evidence-hashed, citation-tagged, and posture-banded from BUILDING to EXEMPLARY.
+12
reliability per quarter
Aggregated across seven BFSI design partners. Methodology on request.
GOVERNED AUTO-TRAINING
Retraining is a governance event, not a project.
A Champion → Challenger pipeline opens automatically for every model the Steward watches. Each transition — Shadow, 95 / 5 split, full promotion — is gated by a written approval on the Stage Gates page. The validator desk approves, it no longer drafts.
0
validator churn
Every transition recorded with a side-by-side reliability delta and a signed rationale.
AUDIT BY DEFAULT
Every model carries its own audit-trail.
Reliability, drift, fairness and remediation actions are evidence-hashed at the moment they occur. A regulator question on March 14th is answered in one expiring URL — the gate state, the agent that acted, the evidence pack, and the reviewer signature, all replayable.
1
URL · zero-prep audit
SR 11-7 §V.A · RBI MD-ITG §7 · BCBS 239 P3 · EU AI Act Art. 10 · DPDP §11.
Inside the platform
Folio · Agents · 04
The agents that run the operating model.
Four surfaces from the live platform — one for each pillar of the operating model. Real screenshots of governed agents working against real models and datasets in a tenant workspace.
Agent Studio
BUILD
Charter, capabilities, knowledge sources and validators — published as a governed agent in minutes, ready for a Champion-Challenger pipeline on first deploy.
Time-to-first-agent14 minutes
Model Steward
SUSTAIN
A six-dimensional Reliability Score with four-horizon forecasts and ranked, signed remediations — every governed model in the portfolio chasing 100.
Reliability gain+12 / quarter
Build · Model Agent
BUILD
The workbench for every governed model in the firm — risk tier, lifecycle stage, drift and fairness health, all on one page. Each row deep-links to a deterministic five-stage build recipe.
Models governed16 live
Pre-Model Agent
DATA
Dataset fitness review, lineage closure and signed remediations — no model is ever built on a dataset the Pre-Model agent has not anchored.
Datasets anchored100 %
Click any tile to zoom · or book a working session to see them rendered against your own portfolio.
The Principles
What separates a Sovaryn agent from a generic LLM.
Three architectural commitments. Each one chosen specifically because the market sells the opposite.
01
Every agent shows its work
Each agent action carries the artefact, the citation, and a typed verdict. No paragraph, no paraphrase, no rounded percentage. The validator and the regulator see the same numbers the data scientist saw.
02
Agents that gate, not narrate
A drift agent commits to one of four canonical RCA buckets — population shift, data quality, custom implementation, model design — with the owner team attached. A fairness agent emits a number, not a recommendation.
03
Two reviewers. Zero conflation.
Business and Regulatory reviews live in separate columns of the same drawer. A model that is commercially fine can still fail the regulatory column — and vice versa. Two reviewers, two sign-offs, every override permanent.
Inside the Workbench
Three surfaces, one auditable trail.
A control plane that hints, then proves. Each surface is a complete experience — you only see the depth when you need to.
Fig. 01
The Pre-Model Workbench
Catch the risk before the build.
Leakage, target drift, weak features, fairness exposure — surfaced before training. Stage-wise floors set by the CRO, not a slide. Numbers go to two decimals; verdicts are typed, not narrated.
- —NULL & outlier spike detection vs the registered baseline
- —Schema-drift catch within 200ms
- —Per-feature severity grade (clean / warn / fail)
Fig. 02
The Cascade Pipeline
Seven stages, one deterministic flow.
Inception → Data → Build → Document → Validate → Deploy → Monitor, orchestrated as a single audited trail. Maker-checker enforced. Every signature chained, every override permanent, every fail-closed.
- —Explainability agent wired into Build
- —Documentation agent grounded in real artefacts
- —Validation review created — never auto-approved
Fig. 03
The Defence Layer
Defend any decision, on demand.
Replay any past timestamp. Quote portfolio expected loss in INR. Share an audit view via a single expiring URL — no login, no email thread, no panic. The platform remembers, so your team doesn’t have to.
- —Structured RCA verdicts (4 canonical buckets)
- —Expiring share-links with full state replay
- —Side-by-side autopilot vs human override records
The Cascade
One pipeline. Seven gates. Fail-closed by default.
Each stage is anchored in a real output from the previous stage. There is no ‘skip’ button, even for the administrator.
No.StageSurfaceWhat happens here
01InceptionCharterTier scoring, business case, intake.
02DataPre-ModelDataset fitness, profile, lineage.
03BuildArtefact+ExplainArtefacts, features, explainability artefacts.
04DocumentMRDAuto-drafted MRD with real citations.
05ValidateGateDoc completeness, fairness, floor check, HoMR.
06DeployPromotionRegulatory gate, signed approval, release.
07MonitorLiveDrift, fairness, backtest, alerts, RCA.
Every transition is captured in an immutable audit trail (model_lifecycle_transitions).
From the Field
Four desks. Four problems. Four results.
Composite case studies drawn from active NBFC and fintech engagements operating under RBI MD-ITG, RBI Digital Lending Guidelines, IndAS 109 and DPDP. Names withheld; methodology available on request.
Underwriting Desk
Digital NBFC · NTC
A new-to-credit scorecard, live in nineteen days.
A digital NBFC needed an NTC scorecard for its salaried-segment personal-loan product. The training set was Account Aggregator pulls plus UPI inflow / outflow features for 11 months — no bureau score available for 38% of applicants. Sovaryn cascaded the recipe through Pre-Model fitness, locked the build with a deterministic seed, ran the fairness guard across the six DPDP-protected cohorts and queued the policy memo against the RBI Digital Lending Guidelines. The model went live on day 19. Approval rate on the NTC slice lifted 11.4 points without breaching the segment delinquency band.
NTC approval lift+11.4 pts
Fraud Desk
Fintech wallet · Fraud
A UPI fraud model, watched at the cadence it drifts.
A fintech wallet was losing 47 bps of GMV to mule-account orchestration. Their device-and-velocity model held its AUC for nine days at a stretch, then slid. Sovaryn pulled the streaming feature contract into Pre-Model, registered the lineage, and put the Steward on a 24-hour evaluation tick. Two weeks in, the Steward opened a Shadow → 95 / 5 → full-promotion pipeline against a re-fit challenger the moment PSI on the top three velocity features crossed 0.2. Fraud-loss bps came down 22 in the following quarter.
Fraud-loss recovery−22 bps
Collections & ECL Desk
Retail NBFC · IndAS 109
Stage-2 transitions, caught nineteen days sooner.
A retail NBFC carried IndAS 109 Stage-2 provisions on a quarterly recalibration cadence. The early-warning signal — a meaningful increase in credit risk — was being detected roughly nineteen days after it actually started. Sovaryn put the collections-propensity model on a Champion-Challenger pipeline with daily fairness and stability ticks, and routed Stage-2 trigger drift into the Stage Gates desk for maker-checker sign-off. Median Stage-2 catch lag fell to four days; ECL provision swing per quarter compressed by ₹38 cr.
Stage-2 catch lag19 d → 4 d
Risk Modelling Desk
NBFC + Bank · Co-lending
Co-lending reconciliation, two scorecards reconciled in one.
A mid-cap NBFC and its scheduled-bank co-lending partner ran two independent risk grades on the same loan book. Reconciliation took the analytics team eleven working days a month, and the deal-by-deal discrepancy rate sat at 6.8%. Sovaryn registered both scorecards as governed agents, anchored the shared dataset under the Pre-Model agent, and used the Steward to surface the segments where the two scorecards systematically disagreed. The reconciliation cycle is now a four-hour run; discrepancy rate is below one per cent.
Recon cycle11 d → 4 h
By the Numbers
What a Reliability OS changes.
Pre-Sovaryn baselines aggregated across seven BFSI design partners. Methodology on request.
+12
reliability per quarter
per model in production · evidence-hashed
0
validator churn
every retrain governed at Stage Gates
5.4×
faster validator throughput
pre-validated evidence packs
< 1
week to a regulator pack
one expiring URL · zero-prep audit
Metric
Before
After Sovaryn
Reliability tracking
Quarterly review
Continuous score · 0→100
Reliability tracking
BeforeQuarterly review
AfterContinuous score · 0→100
Retraining
Project (8–12 wks)
Governed event · Stage-Gates approval
Retraining
BeforeProject (8–12 wks)
AfterGoverned event · Stage-Gates approval
Time to deploy a model
90–180 days
21–35 days
Time to deploy a model
Before90–180 days
After21–35 days
Audit-pack response
6–9 months
< 1 week · expiring URL
Audit-pack response
Before6–9 months
After< 1 week · expiring URL
Drift incidents detected
Post-impact
Pre-impact · auto-pipeline
Drift incidents detected
BeforePost-impact
AfterPre-impact · auto-pipeline
Override visibility
Spreadsheets
100% audited · score-weighted
Override visibility
BeforeSpreadsheets
After100% audited · score-weighted
Aggregated baselines · Q3 2025 – Q1 2026 · anonymised
Compliance
Mapped to the frameworks your supervisor cares about.
Every clause is wired to a working control in the workbench — with audit-log evidence inline.
SR 11-7
Federal Reserve
RBI · MD-ITG
India · BFSI
SEBI · IRDAI
India · Capital + Insurance
DPDP 2023
India · Privacy
Basel III
Global · Capital
EU AI Act
EU · High-risk
ISO 42001
AI management
BCBS 239
Risk data
Subscription
Priced on what reaches production.
Every plan ships the full lifecycle. You only scale price as you scale model count.
Pilot
Freefor 15 days
Run one full cascade end-to-end on your data.
- —One governed model
- —Full lifecycle access
- —Email support
Recommended
Growth
$2,500/ month
10–50 governed models per quarter, with a defence layer.
- —Up to 50 models
- —Time-travel + RAG Copilot
- —Slack + email + webhooks
Enterprise
Customannual
Multi-region, SI-led, audit-grade.
- —Unlimited models
- —SSO + SCIM + BYO cloud
- —24×7 incident support
Full capability matrix available on request · Compliance Desk
Editorial Note
Your AI workforce
should already be working.
Run one full Cascade on one of your models with Sovaryn’s agentic workforce. Fifteen days, no credit card, no commercial conversation. If it doesn’t hold up, you walk away with a free audit-grade evidence pack for the model you tested.
Pilot scope
- 01One full Cascade run on one of your models
- 02Auto-drafted MRD against your home regime (SR 11-7 / RBI / EU AI Act)
- 03A validator decision pack signed by your team or ours
- 04A free expiring share-link your supervisor can audit
No credit card · SOC 2 in flight