Generate Predictions worth acting on

Sovereign AI Platform for Actionable Predictions

TRAINING

Train with Predictable Costs

Bypass Hiring OverheadTrain models with your business and data teams, without writing code or requiring a large team of dedicated specialists.
Fixed Platform FeeEliminate unexpected tooling fees with a flat platform price and no external dependency.
Train on Existing ResourcesAvoid runaway cloud bills and costly stack migration by running on your existing infrastructure.
Save Compute CostPrevent infinite loops and failures from wasting compute, with built-in sanity checks for training configuration errors and oversights.

DEPLOYMENT

Put Predictions Straight to Work

Auto Deploy ModelsTransition from trained models to running predictions in a single click, with your preprocessing transformations carried into production rather than rebuilt for it.
Build Team TrustEarn end-users' trust through their direct involvement in modeling and automated documentation.
Block Erroneous OutputsReject out-of-bounds inputs automatically so invalid predictions do not reach downstream business users.
Uninterrupted OperationsPrevent bad inputs from halting downstream work, with automated guardrails that isolate individual data points and state the specific reason.

Tvaritam automated deployment workflow encompassing model encryption, preprocessing refactoring, data and schema guardrails build, drift detection, and model deployment.

COMPLIANCE

Stay Audit-Ready by Default

Automate Compliance RecordsReplace manual write-ups and patchwork records with auto-generated audit logs and documentation.
No Third-Party ExposureBypass processor agreements, vendor risk reviews, and external breach exposure by keeping all data and training inside your environment.
Control ApprovalsKeep approvals documented and in the hands of your experts using business-aligned metrics.

Documentation covering preprocessing, model hyperparameters and accuracy metrics. Audit Log automatically recording user and device details along with timestamp for every deployment and prediction run. Compliance records obfuscated and encrypted to prevent tampering. No third party risk with complete training within your secure environment.

SOVEREIGNTY

Never Compromise on Sovereignty

Maintain Data PrivacyTrain models privately and stop subsidizing competitors through general-purpose models that your data and domain expertise improve.
Secure Competitive MoatSafeguard your models against tampering and reverse-engineering with automated encryption.
Retain Your IP OwnershipRetain access to your IP, as models and artifacts reside within your environment.
Zero External DependencyEliminate reliance on third-party APIs, forced data sharing, or cloud service uptime as your models execute independently in your environment.

Tvaritam sovereign AI architecture keeps data, models, and dependencies within your environment without telemetry leakages, ensuring complete data privacy and model access.

De-risking Enterprise AI Initiatives

Choosing what to predict and confirming your data is ready are business decisions that belong upstream of the platform. We help you settle both at a fixed price.

Already settled them? The platform is yours to run.

Find the Predictions Worth Making

You know predictions can deliver value. Knowing where to start is the hard part. We work with your team to shortlist opportunities that clear four bars:

Clear business impactMaterial impact on revenue or margins.
Feasible scopeSolvable using predictive modeling and data you already have.
Actionable todaySomeone can act on the prediction inside their existing workflow, without new headcount or a process rebuild.
Regulatory compliancePermitted under the regulations governing your sector and its use of AI.

You get a shortlist you can actually start on, not a list of things that would be interesting to try. Sometimes none of your candidates clear all four, and you'll hear that too.

Assess Data Readiness Before You Commit

Projects that start without the right data rarely fail loudly. They stall, or they ship predictions too weak to trust, and teams quietly route around them. We map what the problem needs against what you actually have:

Volume & granularitySufficient historical depth at the exact level decisions are made, such as, daily data for daily calls, store-level for store-level.
Structural integrityVerifiable source data with consistent metric definitions across years, regions, and systems, without systematic coverage gaps.
Feature variationSufficient variation in inputs to reveal their effect on outcome.
Label availabilityHigh-quality target labels for supervised learning.

You get a straight verdict, including when the answer is that your data isn't ready, and what it would take to change that.

Every team that has to sign off

Team training and deploying models

Predictive Modeling without Grunt Work

Architectural control lossAccelerate experimentation without sacrificing flexibility, with fine-grained control over everything from model architecture selection to convergence criteria.
Deployment refactoring burdenEliminate manual code refactoring and guardrail design by generating prediction pipelines with single-click deployment and built-in drift protection.
Data cleansing overheadCut the manual grind while retaining full control through automatic data quality issue detection, built-in visualizations, single-click undo, and flexible data transformation options.
Regulatory compliance hassleAutomate audit readiness through built-in documentation capturing each preprocessing and training step, alongside logs recording every deployment and usage event.
Prediction failure overheadAvoid manual diagnostics and pipeline halts by using automated guardrails that isolate invalid data points and provide detailed reports outlining the exact cause of each rejected input.

Team governing the dataflow

Predictions without Database Risk

Database blockageEliminate spontaneous large, unoptimized queries from blocking your DB, with complete control over query structure and runtime via a data push ingestion architecture.
Data access controlRetain complete control over who accesses which data, as the platform never queries your database.
Data privacyMaintain full data sovereignty, with training and prediction completely within your secure environment and no data or metadata sharing with us.
Data cleansing overheadReduce routine data preparation requests, as modeling teams cleanse in-platform with single-click transformations and automatic documentation.
Internal ML initiative sidelinedYour initiatives get deployed, instead of stalling, with the platform handling deployment, documentation, and guardrails.

Team acting on the prediction

Workflow-Native Predictions with Domain Awareness

Inaccurate prediction riskPrevent costly business errors from erroneous outputs. Domain-constrained modeling restricts implausible outputs, and data points beyond the training range are withheld with the reason stated.
Workflow disruptionAvoid restructuring daily processes through outputs tailored to meet existing operational decision points.
Silent datapoint rejectionCut manual dataset auditing and troubleshooting with an automated report that includes rejected prediction datapoints and exact failure reasons.
Prediction sensitivityExplore prediction stability and test "what-if" scenarios by running instant batch predictions across modified feature inputs.
Prediction pipeline failurePrevent workflow halts caused by invalid data points through automated schema and input validation that isolates errors before execution.
Training responsibilityBuild with confidence using smart baselines, built-in checks to prevent training failures, and detailed video and document resources.

Team managing security and infrastructure

Secure and Sovereign Predictions

Unauthorized DB accessEliminate the risk of RBAC bypass with a push-based data ingestion architecture.
Telemetry data leakagePrevent sensitive metadata, logs, and metric exposure by restricting telemetry to license verification, backed by an air-gapped deployment option.
Vendor data breach riskEliminate third-party data breach risk and the associated data-handling review, as training happens entirely within your environment.
Software tamperingPrevent binary tampering through cryptographically signed packages and automatic manifest verification at every execution.
Model exfiltration & IP theftPrevent reverse engineering and unauthorized model extraction via automated encryption of models and artifacts.
Installation overheadReduce setup friction and dependency-management burden using a unified installer deployable across developer workstations, servers, and VPCs.

Team managing legal compliance

Sovereign and Compliant Prediction by Design

Data privacy law violationsEliminate the risk of third parties accessing or processing user and confidential data by training completely within your infrastructure.
Data exposure threatEliminate data breach risk by keeping all training and prediction within your secure environment, with no telemetry on either.
Incomplete audit trailReplace compliance exposure from missing or incomplete logs with automated logging across training, deployment, and prediction.
Missing documentationSatisfy regulatory evidence requirements through automated documentation spanning data preprocessing to training and accuracy metrics.
Uncompromising IP ownershipRetain complete trained model ownership with explicit contractual rights, stored locally on your servers, and a perpetual license.

Why Now

Compounding cycle from first success through data curation, model accuracy, confidence, wider adoption, to higher return

Nothing about prediction got urgent this quarter. But two assets that belong exclusively to your business, not to shared technology trends, begin compounding from the day you start: your organization's trust in acting on predictions, and data curated for prediction, not just for records. Neither can be acquired later. You can't backfill a measurement nobody took, and no strategy document or vendor can turn skepticism into confidence overnight.

Meanwhile, the decision is not being deferred. Teams in acute pain are already experimenting, accumulating regulatory exposure and redundant effort with no governance in place. All while the cost and complexity of starting are lower than ever, with dedicated ML teams and GPU clusters no longer prerequisites. The first cycle takes weeks, not quarters. Read More →

Why Us

Cloud platforms let you start without provisioning compute. Consultancies bring expertise you can lean on to build end-to-end solutions.

We focus on the specific problems ML can tackle for your business, not applying generic industry playbooks. That means having the candor to say NO when your data is not ready, and laying bare the missing ingredients. When your team hits a roadblock, they work directly with the engineers who built the platform, not a support queue.

Your team focuses on infusing domain knowledge, the differentiated work that makes models accurate and robust. The platform handles the undifferentiated heavy lifting your team would otherwise absorb: deployment plumbing, drift monitoring, building guardrails, event logging, and compliance documentation.

Trade-offs are for secondary objectives and nice-to-haves, not critical aspects. Tvaritam is architected for organizations to build actionable models without compromising data sovereignty or depending on us to keep them running.

How the options differ
Criteria	Consultancy	Cloud platform	Tvaritam
Where your data sits	Their environment during the build	Their cloud	Yours, always
Who maintains it after delivery	Nobody	You, on their terms	Your team
If the vendor goes away	Model Rots	Service ends	Perpetual license keeps running
Who benefits from what you learn	Their next engagement	Their shared model	Your model only
How the next model gets built	Another consulting engagement	Using their platform	Your team, using the same platform
Compliance evidence	Written up after the fact	Their logs, their custody	Generated as you build, on your systems

Ready to build

Deploy the platform directly on your existing compute footprint today.

Uncertain about readiness

Assess your data readiness before you commit resources.