Predictions worth acting on

Verify your readiness before you commit. Build and auto-deploy models that generate trustworthy, audit-ready predictions.

Tvaritam model comparison dashboard

running on your own infrastructure

✓ Full Data Sovereignty ✓ Single-Click Data Transformations ✓ Audit-Ready ✓ Automated Deployment

Sovereign AI Platform for Actionable Predictions

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.
Tvaritam platform in action.

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.

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.

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.