Intelligence isn't the problem. Extracting it from the right models is.
Divyam.AI automatically builds the evals that define your quality, then deploys them, not to a dashboard, but to pick the best-fit ensemble of models for every request.
- Measure your product quality continuously.
- Use open-source models on your own GPUs at a fraction of the cost.
- Right-size every request to the quality you actually need, and cost drops drastically every time.
You focus on your product. We keep you on the highest quality @ minimum cost.
Trusted by enterprise AI teams shipping to production
From a handful of tagged examples, EvalMate automatically creates your evals. EvalMate and Divyam.AI Router / Control Plane enforce your quality rigorously.
Huge cost savings and quality improvement, automatic and continuous, as Divyam.AI keeps suggesting and adopting new models.
Migrate from closed-source providers to open-source models you self-host. Your data stays completely private, never leaving your walls.
You are never locked into a single model or provider ecosystem, never a hostage. Switch or migrate off any model on your terms, any time.
And it compounds: quality that perpetually improves, cost that keeps falling, and your people freed for the work that moves the business.
Two products. One closed loop.
EvalMate turns a few examples into measurable quality criteria and is the system of record for every agent’s quality. Model Router, the production control layer, uses those criteria in its experimentation layer to train a selector that routes each request, weighing each model’s capabilities and price, to the best-fit model, while a reward model scores a sample of production traffic off the critical path.
From a few representative examples, EvalMate turns your preferences into measurable evaluation criteria, not generic benchmarks, so you define quality before you optimize prompts, choose models, or scale agents. It is your system of record for quality, where every criterion, dataset, and change stays versioned and auditable as you refine.
How EvalMate worksRoute each request to the model that clears your quality bar at the best price, across 100+ LLMs, and adopt better models automatically as they launch.
How Model Router worksContinuous: every cycle compounds cost and quality gains.
You tag 100. EvalMate writes thousands.
Creating evals is the hardest part of putting AI into production. From a small set of your representative examples, EvalMate turns your preferences into measurable evaluation criteria and builds a complete evaluation pipeline, the rubric, aligned judge, and reward model, that the Router then applies to every request at a fraction of the cost. EvalMate is also your system of record for quality: every criterion, dataset, judgment, and revision stays versioned and auditable, so your team can refine the bar with full confidence.
- Start with ~100 examples of what “good” looks like. EvalMate turns them into your evals automatically
- Trains an automated evaluator that agrees with your team 92% of the time
- A system of record for quality: every criterion, judgment, and change is versioned and auditable
- Scales to 10,000+ evaluations at 100x lower cost, feeding routing and model fine-tuning
Not just routing. Agent-level intelligence for every call.
Most routers are either lookup tables, rule-based systems, static decisions, or fire-and-forget pipelines. Divyam.AI's Model Router is the production control layer for inference: the most advanced dynamic decisioning system, trained on your data. Its experimentation layer benchmarks candidate models against EvalMate's criteria and trains a selector that understands agent behavior, conversation context, and task structure; the selector picks the best-fit model per request across quality, latency, and cost while the Router logs each request. It combines proprietary, open-source, and locally hosted models on your GPUs, so every model earns its traffic and you are never locked into one provider. A reward model scores a sample of production traffic off the critical path, and results flow back into EvalMate's system of record to recalibrate routing continuously.
- Trained on your data, not generic benchmarks
- Understands agent intent, context, and conversation history
- Customer-specific intelligence that improves over time
- ~50% cost reduction in the first cycle, compounding to ~75% annually
What you also get.
New models launch weekly. You'll never fall behind.
Models are a commodity. The hard part is knowing which one to use. Divyam.AI continuously benchmarks every new model against your workloads, automatically adopts top performers, and retires underperformers. Zero manual testing, zero downtime.
- Auto-benchmark new models against your specific use cases
- Adopt better models in under a day, not weeks
- Eliminate model churn risk with automated evaluation
- Live leaderboard ranked by quality, cost, and latency
Full visibility into every inference decision.
Monitor cost, latency, quality, and throughput across every model and prompt. Catch regressions before they reach production. Know exactly where your AI spend goes.
- Real-time cost and latency analytics
- Quality monitoring with automatic alerting
- Per-model and per-prompt performance breakdown
- Usage reports and spend allocation dashboards
One Platform. Complete AI Infrastructure.
Your apps connect through a single API. EvalMate defines quality; the Divyam Router then measures, routes, and continuously optimizes every request across quality, latency, and cost, automatically.
Every decision is trained on your data, your agents, and your workloads. The intelligence is unique to your organization. No shared models, no generic benchmarks.
Integrate Effortlessly into Your Ecosystem
Seamlessly adapts to AWS, Azure, GCP, or on-prem setups without disrupting workflows. Secure APIs, flexible deployment, and automated model routing for peak efficiency.
SaaS
Get started in minutes with our fully managed cloud platform. Zero infrastructure overhead, automatic updates, and instant access to 100+ models through a single API endpoint.
Privately Hosted
Deploy on your own AWS, Azure, or GCP infrastructure. Full data sovereignty with enterprise-grade security, dedicated resources, and seamless scalability under your control.
On-Prem
Run entirely within your data center for maximum security and compliance. Air-gapped deployments, custom model hosting, and full network isolation for regulated industries.
The Divyam.AI Difference
Without Divyam.AI
- Generic routing that knows nothing about your agents
- Manual evaluation with spreadsheets and vibes
- New model launches mean weeks of re-evaluation
- No visibility into cost, quality, or where spend goes
With Divyam.AI
- Agent-aware routing trained on your data
- Quality intelligence layer that detects drift and governs routing
- New models benchmarked and adopted automatically
- Full observability into cost, latency, and quality per prompt
Frequently Asked Questions
What is Divyam.AI?
Divyam.AI brings evaluation-driven discipline to enterprise AI. EvalMate turns a few representative examples into measurable quality criteria and keeps the system of record for every agent's quality. The Divyam Router, our production control layer for inference, uses that reward model in an experimentation layer to benchmark candidate models and train a selector; the selector then routes each request to the best-fit model across quality, latency, and cost, and the Router logs each request. Off the critical path, the reward model scores a sample of production traffic to catch drift, and the Router adopts better models over time, so every model earns its traffic and you are never locked into one provider.
What is LLM routing, and why does it matter?
LLM routing is the decision process that selects the best model for each request. Instead of sending every prompt to one default model, Divyam.AI chooses the model most likely to meet the required quality at the best achievable cost for that specific task.
How does Divyam.AI reduce inference cost without sacrificing quality?
Divyam.AI routes simpler requests to lower-cost models and reserves frontier models for cases that truly need them. Because the system continuously evaluates outcomes and adapts to model, traffic, and pricing changes, savings compound over time rather than stopping at a one-time optimization.
What does EvalMate do?
EvalMate is Divyam.AI's quality intelligence layer. It turns a few representative examples into measurable evaluation criteria so teams can define quality before they optimize prompts, choose models, or scale agents, and it is the system of record for every agent's quality, tracking criteria, datasets, prompts, results, drift, and improvement over time. The Divyam Router's experimentation layer uses those criteria (via a reward model) to train a selector that routes each request in production, and the reward model scores a sample of that traffic off the critical path to keep quality on track.
What are "gaps in evaluation coverage"?
They are important regions of production behavior not yet adequately captured by the current eval framework. Divyam.AI detects these blind spots so the system can evolve not just its routing decisions, but also what it measures.
How is Divyam.AI different from other routers or eval tools?
Most routers optimize decisioning. Most eval tools optimize measurement. Divyam.AI connects both into a closed loop: quality is measured, drift and coverage gaps are identified, and routing improves in response. The result is customer-specific intelligence that compounds over time.
What is Model Inertia?
Model Inertia is the tendency of teams to stay on their current production model long after better or cheaper options become available. Divyam.AI breaks that inertia by continuously evaluating new models against your quality bar and updating production decisioning accordingly.
Ready to start compounding?
Join the teams shipping AI to production with confidence. Start with a demo or try EvalMate free today.