Scale Your AI from
Prototype to Production
Divyam is the intelligent inferencing layer that autonomously routes every prompt to the optimal model reducing cost, improving quality, and eliminating vendor lock-in.
Trusted by enterprise AI teams shipping to production
You tag 100. EvalMate writes thousands.
Creating evals is the hardest part of shipping AI to production. EvalMate takes a small set of your preferences and builds a complete evaluation pipeline, co-creating scoring criteria, training automated judges, and scaling to thousands of evaluations at a fraction of the cost.
- Start with ~100 examples of what “good” looks like. EvalMate builds your scoring criteria
- Trains an automated judge that agrees with your team 92% of the time
- Scales to 10,000+ evaluations at 100x lower cost than manual review
- Feeds directly into routing and model fine-tuning
Not just routing. Agent-level intelligence for every call.
Most routers are lookup tables. Divyam's is trained on your data. It learns your agents' behavior, understands conversation context, and makes routing decisions with the intelligence of someone who's seen every interaction your system has ever had.
- Trained on your data, not generic benchmarks
- Understands agent intent, context, and conversation history
- Customer-specific intelligence that improves over time
- 50% cost reduction with measurably better quality
New models launch weekly. You'll never fall behind.
Models are a commodity. The hard part is knowing which one to use. Divyam 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. Divyam handles model selection, routing, evaluation, and continuous optimization 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 Difference
Without Divyam
- 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
- Agent-aware routing trained on your data
- Eval co-pilot that builds and runs suites continuously
- New models benchmarked and adopted automatically
- Full observability into cost, latency, and quality per prompt
Frequently Asked Questions
What is LLM routing and why does it matter?
LLM routing is the process of automatically directing each AI request to the optimal model based on the task's complexity, cost constraints, and quality requirements. Instead of sending every prompt to one expensive model, an intelligent router analyzes the request and selects the best model from 100+ options. This typically reduces inference costs by 50–60% while maintaining or improving output quality. Divyam's router is unique because it's trained on each customer's own data, not generic benchmarks.
How does Divyam reduce AI inference costs by 50–60%?
Divyam's Model Router analyzes each incoming request — considering the agent type, user intent, conversation history, and task complexity — then routes it to the most cost-effective model that meets the quality bar for that specific task. Simple queries go to fast, affordable models while complex ones go to frontier models. Combined with continuous evaluation from EvalMate, the system automatically adopts newer, cheaper models as they become available, compounding savings over time.
What is an eval co-pilot and how does EvalMate work?
An eval co-pilot helps AI teams define, measure, and automate evaluation of their LLM-powered applications. EvalMate works in three steps: first, you share about 100 examples of what "good" looks like and EvalMate builds a structured rubric. Then it trains an LLM judge aligned to your quality standards (~92% agreement with human reviewers). Finally, it distills that judge into a compact reward model (~8B parameters) that runs on your infrastructure, evaluating every response at 100x lower cost than manual review.
How is Divyam different from other LLM routers like Microsoft or NVIDIA?
Most LLM routers use generic benchmarks or simple rules to route requests. Divyam's router is trained on your actual production data — it learns your agents' behavior and your quality definition. In a comparative benchmark on MMLU-Pro, Divyam achieved 84% cost savings compared to Microsoft Model Router's 35%, and 18x better accuracy than NVIDIA's LLM Router. The key difference is customer-specific intelligence that improves over time.
What is Model Inertia?
Model Inertia is a term coined by Divyam.AI to describe the tendency of engineering teams to stick with their current production LLM long after better, cheaper alternatives become available. With new frontier models releasing every 3–4 weeks and inference costs dropping roughly 10x per year, a 6-month-old model deployment likely costs 3–5x more than it should. Divyam breaks Model Inertia with a closed-loop system that continuously evaluates new models against your quality bar and automatically optimizes routing.
Ready to Scale Your AI?
Join the teams shipping AI to production with confidence. Start with a demo or try EvalMate free today.