Sarvaswa AI Labs

Enterprise AI, built on data foundations that hold.

We build the data platforms first and the AI that runs on them second, designed around your real constraints and delivered with the documentation your team needs to run them.

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Business value created
$5M+
Business value created
Companies supported
20+
Companies supported
Compute cost cut on a regulated workload
~40%
Compute cost cut on a regulated workload

Trusted by 20+ companies worldwide

MindCorp
Suvit
Robomarketer
DQLabs
AnswerThis
PPS
Ebbiapp
Discodog
Chronoproof
Lexxy
Brea
Capital Edge
Vetty Clinic
Chronoscout
MindCorp
Suvit
Robomarketer
DQLabs
AnswerThis
PPS
Ebbiapp
Discodog
Chronoproof
Lexxy
Brea
Capital Edge
Vetty Clinic
Chronoscout

The data platform first. The intelligence on top of it second.

All services

01

Databricks Intelligence

Document AI, Genie analytics and multi-agent automation on your lakehouse.

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02

Snowflake Intelligence

Document search and natural language analytics on Snowflake Cortex.

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03

Enterprise AI

Designed around data residency, audit evidence, and legacy systems.

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04

AI Agent Development

Agents that hold real tools and complete multi-step work, with approval gates.

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05

AI Automation

Document intake, triage, reconciliation and reporting, verified before it counts.

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06

Chatbot & Conversational AI

Assistants that finish the request, across web, messaging and voice.

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07

LLM Fine-tuning

Models trained on your data, evaluated on your tasks, deployed to production.

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08

Forward Deployed Engineers

Senior engineers embedded in your team, shipping production systems.

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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

From launch to scaled adoption to continuous optimisation, we turn working AI into commercial results. For our clients that has meant 3x faster time-to-market, and an advantage that compounds.

  • Launch & GTM
  • Adoption & scaling
  • Continuous optimisation

Our flagship, embedded engineering

Forward Deployed Engineers

Senior engineers who embed in your team and ship production AI on your stack. Not a deck, not a rented contractor. You meet the engineer before you commit.

Explore FDEsHire AI developers

Shapes of work we have shipped before.

All use cases

Enterprise AI on Databricks

Document AI, Genie analytics and multi-agent automation on Databricks

Enterprise AI on Snowflake

Document search and natural language analytics on Snowflake Cortex

AWS infrastructure

Predictive GPU auto-scaling that cut AI compute cost by 40%

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

Real engagements, measurable results.

Compliance and cost on AWSRegulated enterpriseHigh-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
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Multi-phase engagement

~40%

Reduction in monthly compute cost

100%

Inference inside client infrastructure

24/7

Observability across the GPU fleet

Marketing AI with humans in the loopUS marketing agencyMulti-channel paid media

An end-to-end AI marketing engine with humans in the loop, built for Robomarketer

Robomarketer, a US-based marketing agency, needed campaign operations that did not bottleneck on manual work. We built a complete marketing engine: it writes social posts, designs ad creatives, builds adsets and campaigns, and runs ROI, CPC, and predictive reporting automatically. Humans stay in control through Slack approve and reject commands, and every decision feeds back into the orchestration layer. Continuous competitor research keeps the briefing data-driven, so the agency scales output without scaling headcount.

Stack

AWSCommercial LLM integrationsCustom orchestration layerSlack HITL workflowPredictive reporting
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Named engagement for Robomarketer

E2E

From creative to reporting

HITL

Slack approve/reject gate

24/7

Competitor research running

Tools & technologies

Anthropic
OpenAI
Databricks
Snowflake
Hugging Face
LangChain
Ollama
Pinecone
AWS
Azure
Python
Node.js
Next.js
React
Strands Agents
Anthropic
OpenAI
Databricks
Snowflake
Hugging Face
LangChain
Ollama
Pinecone
AWS
Azure
Python
Node.js
Next.js
React
Strands Agents

Frequently asked.

We work from fundamentals, model architecture, data pipelines, training strategy. We are not gluing API calls together. The result is AI that holds up in production and improves as your data grows.
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.
A working system, the repository, the evaluation harness and a runbook, with knowledge transfer throughout so your team can run and extend it without us.
We build to your SOC 2, HIPAA and region-specific controls, scoped with your security team at the start of every engagement, and take the architecture through security review before the build.
Yes. You can hire AI developers and machine learning engineers from us as an embedded team rather than a fixed-scope project. Our forward deployed engineers join your standups, work in your repos, and ship production AI on your stack. You meet the engineer before you commit, and typical placement takes about two weeks. It is the fastest route to senior LLM and agent engineering capacity without a six-month internal recruit.
Four things, in our view. Does the agency build from fundamentals (model selection, data pipelines, training strategy) or just wrap someone else's API? Can your team run and extend the system after handover, or does it depend on the vendor? Do senior engineers do the work, or juniors behind a partner's pitch? And will they tell you when AI is the wrong answer? Any AI development company can show you a demo. Ask to see what they hand over when the engagement ends.
We build AI agents: systems that reason, plan, call your tools, and complete multi-step work end to end, with evaluation harnesses and human approval gates where the stakes need them. That includes conversational interfaces and customer-facing AI assistants when a conversation is genuinely the right surface. What we do not ship is a thin chatbot wrapper around a public API, because it cannot act on your systems and it gives you nothing to own.
Finding the recurring, machine-checkable work that quietly costs your team hours, then building AI automation that absorbs it: document processing, claims and ticket triage, data pipeline repair, reconciliation, reporting. For enterprises it integrates with legacy systems and carries the governance and audit trails your compliance team already accepts. Every workflow keeps a human approval gate before anything irreversible.

Working with Claude? Skills, MCP, model selection, and deployment are answered on the Claude Enablement FAQ.

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.

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