< Production Accelerators >

Deployable AI,
proven on real clusters

Five pieces of Avashya IP that ship with the engagement and keep running after we leave. Each one came out of a production problem, not a roadmap.

1,056 GPUs tuned4 languages liveYours to run

01 /

AIP

Avashya Intelligence Platform — Agent Observability & Evaluation

Agents in production drift, regress, and fail quietly. AIP watches them, scores them, and breaks them on purpose in a sandbox before your users do.

  • Per-user identity on every call, replacing shared API keys.
  • Automated nightly evaluations in place of manual review.
  • Disaster testing in sandbox before anything reaches production.
  • Full trace of inference traffic, cost, and per-user usage.

Running in a regulated fintech and a BFSI lender.

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Fig. 1 — AIP

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Voice AI Solutions

Customer Support, Outbound Calling & Appointment Booking

Autonomous voice agents that negotiate, book, and escalate. Built on a hybrid AWS and open-source stack, so the model layer is swappable and the cost curve is yours.

  • Hindi, English, Hinglish, and Kannada NLU, 24/7.
  • Hundreds of parallel conversations, targeted 0.5–1.5s latency.
  • Warm handoff to a human agent on threshold breach.
  • Fine-tuned TTS benchmarked against a ≤500ms P95 target.
  • One deployment traded a ₹45 Cr / 18-month API trajectory for ~$12.4K/month on SageMaker.

Live in a freight marketplace and a consumer social platform.

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Fig. 2 — Voice AI Solutions

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GPU Optimizer

Training & Inference Cluster Tuning

Large GPU fleets fail silently. Dropped EFA traffic, unattached interfaces, storage regressions, collectives that hang past a node count. This is the tuning and the version-locked stack that stops that.

  • Proven at 132 nodes and 1,056 NVIDIA B200 GPUs in production.
  • GDS throughput restored from ~12 GB/s to ~35 GB/s.
  • Stable multi-node NCCL all-reduce across the full fleet.
  • Version-locked driver stack: EFA device plugin, nvidia-fs, containerd limits.
  • Single-node canary before any fleet-wide rollout.

Built on a frontier LLM lab’s production training cluster.

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Fig. 3 — GPU Optimizer

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Frontier Agents

Autonomous DevOps, SRE, Security & Incident Management

Agents that close the loop themselves: detect, diagnose, remediate, verify. Scoped to what you grant them, with a decision trace on every action.

  • Detect → diagnose → remediate → verify, with no human restarting the cycle.
  • Coverage across DevOps, SRE, security, and incident management.
  • Every action leaves an auditable decision trace.
  • Deployed under scoped IAM against your own account.

Avashya IP. Engagement metrics not yet published.

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Fig. 4 — Frontier Agents

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Coding Agent Optimizations

Governed Claude, Codex & Cursor Workflows

Your engineers are already using coding agents. This puts that traffic inside your security perimeter without slowing anyone down.

  • Self-hosted inference gateway on EKS behind an ACM-secured ALB.
  • OIDC SSO (Okta, Keycloak, JumpCloud) with domain-restricted auth.
  • Credential-less Bedrock access via IRSA. No shared secrets.
  • Surface-aware logging that separates CLI from Desktop usage.
  • Tuned workflows for Claude, Codex, Cursor, and other agents.

In production at a regulated fintech.

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Fig. 5 — Coding Agent Optimizations

< And five you never see >

The other five run inside delivery

Inventory assessment, dependency mapping, TCO modeling, LLM migration and evaluation, and a Well-Architected baseline scanner. They cut discovery from weeks to days. You get the output, not the tool.

See all ten

Want one of these on your account?

Thirty minutes with a founder. Describe the workload and we will tell you which of these applies to it and which does not.