Sarvaswa AI Labs
Enterprise AI & Data Engineering

Enterprise AI, built on data foundations that hold.

Most organisations do not have an AI problem so much as a data problem wearing an AI costume, because the ambition is usually clear while the information needed to act on it sits across a dozen systems that were never designed to agree with one another. We build the data platforms first and the intelligent applications that run on them second, working inside your own cloud and against your real constraints, so what reaches production is a system your team operates rather than a pilot that stalls at the security review. You own the platforms, the models, and the pipelines when we are finished.

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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
How we partner

Navigating data complexity, together.

Fragmented data is rarely the result of bad decisions. It is usually the accumulated residue of good ones, made at different times by different teams working under different pressures, which is why untangling it calls for somebody willing to sit with the people who built it rather than issue a target architecture from the outside and wait for the organisation to comply.

We work alongside your team through that process instead of around it, and we measure the result the way your finance function does, in cost removed and revenue enabled rather than in models deployed. On a recent regulated workload that meant roughly forty percent off monthly compute while every piece of inference stayed inside the client's own infrastructure.

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

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 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.

CORE

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.

Flagship · Embedded engineering

Forward Deployed Engineers

Senior engineers who embed in your team and ship production AI in your infrastructure. Your stack, your IP. Not a deck, not a rented contractor.

Explore FDEs

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

MARKETING AI · HUMAN-IN-THE-LOOP·US marketing agency·Multi-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
Talk to the engineers behind this

Named engagement · Robomarketer

E2E

From creative to reporting

HITL

Slack approve/reject gate

24/7

Competitor research running

HEALTHCARE · FLAGSHIP PRODUCT·Clinics & healthcare practices·Multi-location front desks

Lisa, an AI receptionist that turns the clinic front desk into 24/7 capacity

Lisa is Sarvaswa's flagship product: an AI receptionist built for clinics and healthcare practices. She understands callers in natural conversation, books appointments, answers questions about the practice, and updates the CRM in the background. Clinic owners get fewer no-shows, fewer dropped calls, and a reception layer that scales with practice growth without changing staffing. Lisa runs on the same voice infrastructure we engineered around a 95ms speech-to-speech round-trip target.

Stack

Voice AI runtime (ASR · NLU · TTS)Appointment bookingCRM integrationAfter-hours triage
Talk to the engineers behind this

Flagship product

24/7

Reception capacity

95ms

Voice round-trip target

Live

CRM updated in the background

Tools & technologies

Anthropic
OpenAI
Hugging Face
LangChain
Streamlit
Ollama
Pinecone
AWS
Azure
Flutter
Python
Node.js
Next.js
React
Strands Agents
Anthropic
OpenAI
Hugging Face
LangChain
Streamlit
Ollama
Pinecone
AWS
Azure
Flutter
Python
Node.js
Next.js
React
Strands Agents
Questions worth answering

Frequently asked.

We work from fundamentals, model architecture, data pipelines, training strategy. We are not gluing 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. We build to your SOC 2, HIPAA and region-specific controls, scoped with your security team at the start of every engagement.
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 inside your infrastructure. 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? Do you own the models, code, and pipelines at handover, or are you renting them? 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 we run it inside your cloud and your data boundaries, integrated with legacy systems, with governance and audit trails your compliance team already accepts. Every workflow keeps a human approval gate before anything irreversible.
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.

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