AI is inevitable. Production is engineered.
AI transformation, done end to end. Avashya takes enterprises from proof of concept to production, so AI doesn’t stall at the demo.
To bridge the gap between AI innovation and real-world impact by engineering secure, scalable, production-ready solutions that help businesses transform, modernise, and grow.
Backed by:
Partners
The principlesThe Stack
Six principles. 1 production-ready stack.Six principles.One production-ready stack.
View ServicesOutcomes are engineered, not advised
We are accountable for a use case running in production against a business KPI, not for a deck or a headcount.
Transformation spans the whole stack
Compute, models, data, agents and trust controls are designed together, because the failures happen between them.
Own what matters, rent the rest
We stay neutral across models and clouds and engineer the cost of ownership honestly, including self-hosting open models when the economics and data sensitivity justify it.
Every engagement makes the next one faster
What the field teaches us becomes reusable platform, so embedded work does not decay into bespoke consulting.
Governance is architecture
Access control, evals, guardrails and audit trails are built into every layer before launch, not added after the first incident.
The unit of delivery is an embedded pod
A small team of forward deployed engineers sits inside the business, ships on live data, and stays until your people can run the system.
- 01
Assess
Discover workloads and map their dependencies.
24 VMs across two GCP projects
- 02
Model
Project cost and optimize AWS pricing before you commit.
SageMaker cost weighed against a commercial API
- 03
Build
Deploy infrastructure as code on AWS.
two-stage Terraform rollout across 132 nodes
- 04
Prove
Test quality, security and posture before cutover.
side-by-side model benchmarking before cutover
- 05
Operate
Run, optimize and govern cost.
full visibility into inference traffic and cost
Case studiesCase studies
From complex challenges to production-ready solutionsFrom complex challenges to production-ready solutions
View Case Studies- SarvamAI research
The solution focused on optimising Sarvam AI’s GPU infrastructure on AWS by fixing EFA networking, storage throughput, and multi-node communication issues. Avashya implemented a staged Terraform deployment, secured EFA networking, patched NVIDIA storage components, and validated stability through a canary rollout before scaling to 132 nodes. Observability was added through DCGM and Grafana, resulting in 1,056 NVIDIA B200 GPUs ready for production, with GDS throughput improving from approximately 12 GB/s to 35 GB/s.
- DezervFintech
Avashya built a secure, self-hosted AI gateway on Amazon EKS, replacing shared API keys with individual authentication, SSO, and centralized monitoring. This enabled auditable AI usage, improved cost visibility, and secure Claude adoption aligned with fintech compliance requirements.
- SaveSageEdtech
Avashya migrated SaveSage from GCP to AWS in seven weeks, moving compute, databases, and 7 TB of storage while modernising networking and security. The migration achieved zero downtime, improved scalability, and consolidated infrastructure on AWS at an indicative run rate of $12,300 per month.
- EnterpretFeedback analytics
Avashya fine-tuned Qwen3-8B using QLoRA to automate feedback classification, improving accuracy from 0.19 to 93.9% F1 in production. Through threshold calibration and improved training strategies, the solution enabled reliable, scalable feedback tagging without requiring additional data collection.
Let’s put your AI to work
From production infrastructure to agentic systems, tell us what you’re building and where you need momentum.
hello@avashya.tech
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