Sarvaswa AI Labs
AI Consulting & Engineering

AI solutions for today's businesses

We guide your business through every stage of AI implementation, from initial strategy and model selection to production deployment and scaling.

Book a callExplore services

Trusted by 20+ teams worldwide

MindCorp
Suvit
Robo Marketer
DQLabs
AnswerThis
Lexxy
PPS
Ebbiapp
Discodog
Vetty Clinic
Chronoscout
MindCorp
Suvit
Robo Marketer
DQLabs
AnswerThis
Lexxy
PPS
Ebbiapp
Discodog
Vetty Clinic
Chronoscout
How we deliver

A proven framework, refined across 20+ companies.

01

Strategy

Clarify your highest-value AI opportunities and set a roadmap that minimises technical risk and maximises return; starting with first principles, not vendor pitches.

  • Opportunity discovery
  • Roadmap & costing
  • Risk and ROI modelling
02

Development

A team of AI engineers and designers build scalable, custom AI for your business, from prototypes to enterprise-grade systems that hold up under load.

  • Custom LLMs & agents
  • Data pipelines
  • Production-grade architecture
03

Commercial

Turn AI into a market-winning product. From launch to scaled adoption to continuous optimisation, we help you turn innovation into a lasting competitive advantage.

  • Launch & GTM
  • Adoption & scaling
  • Continuous optimisation
Our process

How we work, step by step.

01

Discover

We learn your business, data and goals before any code. The right solution, not the fastest one.

02

Design

Architecture, model selection, and data strategy designed for your specific use case and scale.

03

Build

Engineers and designers ship production-grade AI with the right tradeoffs for cost, latency and accuracy.

04

Scale

We stay beside you, optimising performance and expanding capability as your business grows.

$5M+

Business value created

3x

Faster time-to-market

20+

Companies supported

25+

Years of collective expertise

What you gain

Everything you need to ship real AI products.

FLAGSHIP

Custom LLMs you actually own.

Trained on your proprietary data, evaluated against your real use cases, deployed inside your infrastructure. Not a wrapper around someone else's model, a defensible AI moat that compounds.

Fine-tuning
Evaluation
Deployment

AI Strategy & Alignment

Define your AI vision, structure projects, and align stakeholders with focused 1-on-1 consulting.

Roadmap & Costing

A document covering scope, costing, ROI and timelines, the kind that survives a CFO review.

Agents & Automation

Multi-agent systems that reason, plan and act, replacing manual work end-to-end.

Testing & Launch

Validation, safety, and performance metrics so launch isn't a leap of faith.

Continuous advisory & support

Executive-level AI advising as markets, models, and your product evolve. We stay beside the team after launch, not just at handoff.

What teams say

Trusted by founders and engineering leaders.

The Sarvaswa team was professional, responsive, and technically deep on our chatbot build. They communicated clearly, shipped regular updates, and handled every change request with a positive, problem-solving attitude. We'd work with them again on similar projects without hesitation.

Ramesh - Director of Engineering

BillionApps Inc

Sarvaswa has been excellent to work with — they built the AI app at the heart of FixMyAir end-to-end. Their depth in AI agents and machine learning is the real deal. Genuine experts who treat your business like their own.

John B. - Founder

FixMyAir

Selected work

Real engagements, measurable results.

AWS · COMPLIANCE & COST·Regulated enterprise·High-throughput AI workload

A predictive GPU auto-scaling engine and a domain-trained SLM, ~40% lower compute cost

The client needed a domain-aware language model that kept sensitive data inside their own infrastructure and out of commercial AI APIs, a compliance requirement for their regulated workload. We trained a small language model (SLM) on their proprietary data, deployed it on AWS EC2 GPU instances, and built a predictive auto-scaling engine that forecasts transactions-per-minute from historical patterns and scales the GPU fleet ahead of demand. The fleet runs lean by default, and continuous observability gives the team end-to-end visibility into utilisation, cost, and forecasted load.

Stack

Domain-trained SLMAWS EC2 (GPU)Predictive autoscaling engineTime-series forecastingCloudWatchTerraform
Talk to the engineers behind this

Multi-phase engagement

~40%

Reduction in monthly compute cost

100%

Inference inside client infrastructure

24/7

Observability across the GPU fleet

Tools & technologies

OpenAI
Anthropic
Hugging Face
LangChain
Streamlit
Ollama
Pinecone
AWS
Azure
Flutter
Python
NodeJs
NextJs
ReactJs
Strands Agents
OpenAI
Anthropic
Hugging Face
LangChain
Streamlit
Ollama
Pinecone
AWS
Azure
Flutter
Python
NodeJs
NextJs
ReactJs
Strands Agents
Questions worth answering

Frequently asked.

We work from fundamentals, model architecture, data pipelines, training strategy. We are not glueing API calls together. The result is AI you actually own and can defend long-term.
Both. For startups we act as a technical AI co-founder; for enterprises we ship measurable agentic and ML systems that integrate with legacy stacks.
We start with a focused 1-2 week discovery, deliver an AI roadmap with costing and ROI, then scope build phases. Most clients move from kickoff to production in 8–14 weeks.
Yes. Full ownership, full source. The competitive moat is yours, not ours.
We work inside your cloud, with your data boundaries. SOC 2, HIPAA and region-specific requirements are scoped at the start of every engagement.
Working with Claude?

We've got a dedicated FAQ for Claude.

Skills, MCP, model selection, ownership, deployment — the questions teams ask before working with us on Claude.

Claude Enablement is helping a team turn Anthropic's Claude into a real production capability, not a demo and not a chatbot wrapper. At Sarvaswa we deliver four pieces in every engagement: custom Claude Skills tailored to the team's domain, MCP (Model Context Protocol) servers running inside the team's perimeter, the context engineering work that keeps the model reliable (retrieval, prompt registry, evals), and the operational runbook the team uses to ship and operate Claude on its own.
MCP, the Model Context Protocol, is the open standard Anthropic created so Claude (and other models) can talk to your tools, databases, and APIs through a uniform interface. It matters because it lets Claude act on your systems instead of just answering, while keeping the integration layer inside your infrastructure. We build MCP servers that live in your cloud, behind your auth, so sensitive data never leaves your perimeter.
It depends on the workload. We default to Claude Sonnet for most production agents; fast, cheap enough at scale, smart enough for tool use. Opus comes in for genuinely hard reasoning tasks where one strong answer is worth several Sonnet calls. Haiku is for high-volume, low-latency workflows where most calls are pattern-matching. Model selection is a first-class deliverable in every engagement, not an afterthought.
See all Claude FAQs

Let's shape your AI strategy together

Whether you have a clear spec or just an idea, we can help you figure out the right approach and build it the right way.

Book a call