AI product engineering
End-to-end AI products: agents, copilots, structured-output pipelines, and evaluation loops engineered for real workloads.
- Agents & tool use
- Structured outputs
- Evaluation harnesses
AI product studio · led by Aditya Gaikwad
AI products, apps, and platformsbuilt from idea to production.
Soma Tech Labs helps founders, institutions, and teams design and build serious software: AI workflows, RAG systems, mobile apps, backend platforms, and developer-grade product infrastructure.
Public flagship initiative: IndiaLearn.org — a multilingual AI education platform for India. Select product work remains private until launch.
The Soma AI Product Stack
v1- L1
Interface
Mobile, web, voice — the surface users meet.
- L2
Intelligence
Models, agents, prompts, structured outputs.
- L3
Data
Retrieval, embeddings, pipelines, knowledge graphs.
- L4
Infrastructure
Backends, queues, deploys, observability.
- L5
Evaluation
Harnesses, safety, telemetry, real-world loops.
Five layers, one product. Every Soma build is reasoned about across all of them — not just the surface.
Capabilities
A senior AI product lab, end-to-end.
Soma Tech Labs covers the full surface area of an AI-first product: architecture, retrieval, mobile, backend, developer experience, automation, and consulting.
RAG & knowledge systems
Production retrieval systems with pgvector, hybrid search, careful chunking, and domain-aware reranking.
- pgvector / Neon
- Hybrid retrieval
- Domain reranking
Mobile app development
Native-feeling cross-platform apps with React Native and Expo. On-device inference where it matters.
- Expo / React Native
- On-device ML
- Offline-first UX
Backend & platform architecture
Typed, observable backends on NestJS, Prisma, and PostgreSQL. Built to scale without rewriting.
- NestJS + Prisma
- Postgres / Neon
- Queues & workers
Developer experience & DevRel systems
Documentation systems, onboarding flows, troubleshooting trees, and release comms for technical products.
- Docs architecture
- Onboarding flows
- Release comms
Automation & internal tools
Internal AI copilots, batch pipelines, and operations tooling that compounds team leverage.
- Batch pipelines
- Internal copilots
- Ops dashboards
Product strategy & technical consulting
Architecture reviews, scope sharpening, and roadmap design for AI-heavy product bets.
- Architecture review
- Scope sharpening
- Roadmap design
Services
Concrete engagements with concrete outcomes.
Each engagement is scoped to ship — not a deck, not a workshop, a working system you can run.
Build an AI MVP
Take an AI idea from concept to a shippable, demoable product with real users and real data.
- Working MVP in weeks, not quarters
- Production-grade architecture from day one
- Clear path to scale and iterate
Rebuild or modernize an existing app
Replace fragile legacy code with a typed, observable system that engineers actually want to extend.
- Modern stack, no rewrites mid-flight
- Migration without downtime
- Documented, testable, ownable codebase
Design an AI architecture
An end-to-end architecture for your AI product: models, retrieval, evaluation, safety, and cost.
- Concrete reference architecture
- Model and vendor selection
- Evaluation and guardrails plan
Build a RAG / knowledge system
A retrieval system tuned to your domain — ingestion, embeddings, search, and answer quality you can measure.
- Reliable retrieval over your corpus
- Domain-tuned reranking
- Evaluation suite you can trust
Launch a mobile app
A polished iOS and Android app on Expo with the AI features your users actually need.
- Single codebase, both stores
- On-device inference when needed
- Telemetry and analytics wired in
Build developer / platform tooling
SDKs, CLIs, docs systems, sandboxes, and internal platforms that scale developer leverage.
- Typed SDKs and clean APIs
- First-class documentation
- Onboarding flows that convert
Technical audit & roadmap
An honest read of your current system and a sequenced roadmap to where you want to go.
- Independent architecture review
- Prioritized risk register
- Sequenced delivery plan
Public case study
IndiaLearn.org
A multilingual AI education initiative for India's Tier 2 and Tier 3 cities — production AI workflows applied to learning at scale.
Gemini APIGoogle AI StudioGCPRAGpgvectorOpenAI embeddingsClaudeStructured outputs
- 01
Problem
Quality technical education stops at the language barrier.
Across India's Tier 2 and Tier 3 cities, the best AI and engineering material is gated by language, bandwidth, and an assumed cultural context that doesn't match the learner on the ground.
- 02
System
Production AI workflows, wired into learning.
Gemini API, Google AI Studio, GCP, RAG over pgvector, OpenAI embeddings, Claude, and structured outputs — composed into content pipelines that ship multilingual learning material at scale.
- 03
Public initiative
Openly named, openly accessible.
IndiaLearn.org is the publicly named flagship initiative. The systems thinking — multilingual pipelines, retrieval, evaluation — is meant to be reviewable in the open.
- 04
India-first infrastructure
Built for the learner on the ground, not retrofitted.
Curriculum, languages, and delivery shaped by Indian learners and Indian devices — architectural choices that respect bandwidth, surface, and context, rather than ports from elsewhere.
Confidential R&D
Work we cannot name yet — but can describe carefully.
Some of our deepest R&D remains private until launch. Publicly, we share the systems thinking: local-first architectures, safety-gated workflows, mobile AI interfaces, and domain-specific intelligence layers.
Private until launch
Privacy-first mobile AI systems
Mobile architectures where sensitive context stays on-device by default, with explicit, review-gated escalation paths.
Private until launch
Domain-specific intelligence layers
Retrieval, reasoning, and evaluation tuned to a single domain rather than generic chat surfaces.
Private until launch
Local-first, safety-gated architectures
Inference that runs locally where it should and escalates carefully where it must, with guardrails in between.
Private until launch
Review-gated product workflows
Human-in-the-loop checkpoints designed into the product, not bolted on after the fact.
Private until launch
Mobile AI UX prototypes
Interaction patterns for AI-native mobile surfaces — fast, calm, and built for the long session, not the demo.
We do not disclose product names, partner identities, or implementation specifics for unreleased work. Under appropriate context — and where useful for an engagement — we can share more.
Founder
Aditya Gaikwad — AI platform engineer and technical community operator.
An operator who can both build and communicate complex systems. Five-plus years scaling global developer ecosystems, debugging infrastructure at protocol scale, and now building production AI systems for India-first and global products.
Scaled the Iron Fish developer community from 3,000 to 60,000+ members, serving as the primary bridge between protocol engineers and node operators across the network.
Debugged 500+ infrastructure issues spanning CLI, configuration, networking, and logs. Built the onboarding systems, troubleshooting trees, migration guides, and release communications that turned a protocol into a usable platform.
Today, Aditya designs and builds production AI systems — multilingual content pipelines, retrieval architectures, mobile AI surfaces, and the developer-grade infrastructure underneath.
SpeaksEnglishHindiMarathiGujarati
Aditya Gaikwad
Founder · AI Platform Engineer
By the numbers
- 3K → 60K+
- Developer community scaled
- 500+
- Infrastructure issues debugged
- 5+
- Years in developer ecosystems
- 4
- Languages spoken
Current stack
- Gemini API
- Google AI Studio
- GCP
- RAG
- pgvector
- OpenAI embeddings
- Claude
- TypeScript
- Next.js
- NestJS
- Prisma
- Expo
- PostgreSQL
- Neon
- Docker
- Turborepo
- Python
Process
How we work — diagnose, architect, ship, iterate.
A repeatable process for AI product engineering. Every stage produces something concrete you can review, evaluate, and run.
- 01
Diagnose
Understand the real problem, the constraints, and what success looks like. No assumptions left implicit.
- 02
Architect
Design the system: data, models, services, surfaces, and the trade-offs we are choosing.
- 03
Prototype
Build the smallest thing that exercises the hardest unknowns. Validate before we commit.
- 04
Build
Production engineering: typed, observable, tested. Surgical changes, clear commits, shippable increments.
- 05
Validate
Evaluation harnesses, real-user testing, safety checks, and load characteristics measured.
- 06
Launch
Release engineering: rollout plan, telemetry, runbooks, and the communications that matter.
- 07
Iterate
Post-launch loops: usage signals, model updates, retrieval tuning, and product evolution.
Engagement models
Ways to work with Soma Tech Labs.
Pick the shape of the engagement that matches the stage of your problem.
Fixed-scope MVP
A defined product built to a defined outcome on a defined timeline.
Best for
Founders with a sharp idea and a deadline.
Product build sprint
A focused multi-week sprint to ship a critical surface, feature, or migration.
Best for
Teams with momentum that need a senior push.
AI architecture review
Independent review of your AI architecture, retrieval, evaluation, and cost posture.
Best for
Teams about to scale, raise, or rebuild.
Fractional product / AI engineering
Senior engineering presence across architecture, code review, and delivery on a recurring cadence.
Best for
Early teams without a senior AI lead yet.
Long-term build partner
A multi-quarter engagement co-owning roadmap, architecture, and shipping with your team.
Best for
Serious product bets that need a real partner.
Start a project
Have a serious build in mind?
Tell us what you're building. We'll respond with what would make sense as a first step.
01
Send a short note
What you're building, the rough shape, and any constraints.
02
Quick reply
A real response — usually within a day or two, often the same day.
03
Scoping call
If there's a fit, a focused call to map the next concrete step.
Confidential product work disclosed only under appropriate context.