01
From fundamentals
We understand AI at the model level, not just the API level. Stronger architectures, better performance, solutions that hold up as you scale.
How we fine-tune modelsAn AI development agency that builds AI from fundamentals and ships it to production.
Sarvaswa AI Labs designs, trains and ships custom AI for startups and enterprises: fine-tuned models, agents, data pipelines and the production systems around them. The team works from Surat and Bengaluru with clients across the Americas, Europe and Asia. Everything we build is designed for production and handed over with the documentation your team needs to run it.
Why we started
The gap we kept seeing.
After many years working alongside enterprise companies and fast-moving startups, we kept seeing the same gap. Companies had strong engineering teams, senior developers, product leads, capable CTOs. But when it came to applying AI to their specific domain, something would break down.
Not the talent. The decisions.
AI architecture is different. The choices you make at the foundation, model selection, data pipelines, training strategy, deployment architecture, compound over time. A wrong turn early means months of rework later. Most teams learn this the hard way.
We started Sarvaswa AI Labs to be the partner that sits beside your team and makes those decisions right. Not a vendor you hand work off to. A technical co-founder who has done this before, who understands your business goals, and who builds AI that holds up in production.
Most teams use AI as a layer on top of existing software. We build from fundamentals: stronger models, better performance, and systems that keep improving as your data grows. That is the difference between using AI and building with it.
12
Specialised AI engineers
20+
Companies supported worldwide
$5M+
Business value created
25+
Years of collective expertise
Our approach
How we think about building AI.
02
Side by side
We embed with your team. Architecture reviews, design sessions, implementation, present throughout, not just at handoff.
Forward Deployed Engineers03
Built to last
The systems we launch are designed to be run, extended and improved by your team long after we step back.
Enterprise AI serviceHow we work
How an engagement runs, from first call to handover.
Every engagement follows the same four steps. The first two are short and deliberately cheap, so you know what you are building and what it will cost before real money is spent.
- 01
Discover
One to two weeks
We learn the business, the data and the goal before any code. You get a scoped plan with costing and expected return.
- 02
Design
Inside discovery
Architecture, model selection and data strategy designed for your use case and your scale, reviewed with your engineers.
- 03
Build
Eight to fourteen weeks to production
Engineers ship production-grade AI with the right trade-offs for cost, latency and accuracy, integrated with your systems.
- 04
Scale
After launch
We stay beside the team, tuning performance and extending capability as the business grows, until your team runs it alone.
Every engagement starts with a call with Mitul Sarvaiya, the founder, rather than a sales team. You will meet the engineers who would do the work before you commit to anything.
Who we work with
Built for startups and enterprises.
For startups
Your technical AI co-founder.
We become your technical co-founder from day one. We design your system architecture, choose the right stack, and build your AI product from idea to market, so you can focus on growth.
- System architecture and design
- Tech stack selection
- AI product from idea to launch
- Full technical accountability
For enterprises
AI for enterprise, built for real business impact.
We build enterprise AI that removes manual work and adds capacity the team did not have. From agentic workflows to custom models, it integrates with the systems you already have and keeps the audit trail your compliance team expects.
- Agentic workflow automation
- Custom model development
- Legacy system AI integration
- Revenue and customer-experience impact
Selected work
What building from fundamentals looks like in production.
Compliance and cost on AWSRegulated enterprise
A predictive GPU auto-scaling engine and a domain-trained SLM, ~40% lower compute cost
A regulated client could not send sensitive data to a commercial AI API. We trained a small language model on their proprietary data, served it on GPU instances in their AWS account, and built a predictive auto-scaling engine around it. Compute cost fell by about forty percent.
Stack
Domain-trained SLMAWS EC2 (GPU)Predictive autoscaling engineTime-series forecastingCloudWatchTerraform
~40%
Reduction in monthly compute cost
100%
Inference inside client infrastructure
24/7
Observability across the GPU fleet
About Sarvaswa, answered.
Sarvaswa AI Labs is an AI development agency. We design, train and ship custom AI for startups and enterprises: fine-tuned and small language models, AI agents and automation, chatbots, data pipelines on Databricks and Snowflake, Claude enablement with Skills and MCP servers, and the production infrastructure around them. Engineers can also embed directly in a client team as Forward Deployed Engineers.
The team works from Surat and Bengaluru in India, with twelve specialised AI engineers. Clients are across the Americas, Europe and Asia, from startups that need a technical co-founder to enterprises in regulated industries such as financial services, healthcare, insurance and legal.
With a fifteen-minute call with the founder. If it makes sense to continue, a one to two week discovery follows: we learn the business, the data and the goal, and you get a scoped plan with costing and expected return before any build spend. Most builds then reach production in eight to fourteen weeks.
A working system and everything needed to run it: the repository, the models, the pipelines and the runbook, with knowledge transfer throughout the engagement. There is no Sarvaswa-hosted layer you keep paying for, and your team can extend the system without us.
It means we make the foundation decisions deliberately rather than gluing API calls together: which model, whether to fine-tune or retrieve, how the data pipeline is shaped, where inference runs. Those choices compound. Getting them right early is the difference between a demo and a system that holds up as usage grows.
Book a fifteen-minute call directly, or write to hello@sarvaswa.ai. First response is within twenty-four hours, and we are available for new engagements.