AI/MLDataWebMobile

AI that ships.Not AI that demos.

The Stack Hub is an AI/ML and data engineering studio. We build production machine learning, LLM applications, and the data platforms behind them — then ship them as web and mobile products people actually use.

Start a project See what we build

Fixed-fee discovery. Two-week delivery increments. You own every commit.

Fig. 01Data → decision

Every engagement runs this shape. We join at whichever stage is broken — and most of the time, it is not the model.

Python
PyTorch
scikit-learn
LangChain
Hugging Face
pgvector
dbt
Airflow
DuckDB
Postgres
Snowflake
Kafka
TypeScript
Next.js
React
React Native
Expo
Node.js
AWS
GCP
Docker
Terraform
Vercel
GitHub Actions

01Our products

Two agentsunder one roof.

One reads what happened on your screen and hands your coding agent the evidence. The other lives in every text field you already type in. We build the studio’s own software to the standard we sell — these are the closest thing to reading our commits.

01Open source

Framesleuth

Turn any video into code your agent can ship

Record the bug once. Framesleuth reads the frames, the console, the network traffic, and the narration, then hands your coding agent a structured, evidence-cited Context Bundle instead of a paragraph of guesswork.

Nothing leaves your machine. Secrets and PII are redacted before anything is written.

Pipeline: 9 stages
MCP tools: 14, read-only
Processing: Fully local
Licence: Apache-2.0

Star it on GitHub framesleuth.com/framesleuth

Apache-2.0 · Free forever, self-hosted

Framesleuth

Local only

framesleuth analyse bug-report.mp4

01preprocesscontainer → frames
02transcribeaudio → transcript
03keyframesframes → keyframes
04understandkeyframes → captions
05classifysignals → label
06extractevidence → bundle
07redacttext → safe text
08summarizebundle → narrative
09grounderror text → file:line

context-bundle.jsonevidence-cited, versioned

What it runs on

Local vision model for OCR and UI state
faster-whisper transcription
Frame-to-frame visual-delta scoring
Embedding fusion for code search
Read-only over your workspace
Degrades gracefully when a component is missing

02In development

Framesleuth Sidekick

Every text field on the web, in 15 languages

A browser extension that replies, translates, fixes, and rewrites wherever you already type — plus OCR, image-to-diagram, chart analysis, and page digests. Thirteen capabilities, no API key, and a preview you approve before a single character is inserted.

Preview before insert. Nothing is ever auto-sent.

Capabilities: 13
Languages: 15
Surface: Every text field
History: Local to your browser

See SidekickComing to the Chrome Web Store

Sidekick

15 languages

Drafts a response in the thread you are already reading.

Incoming message

Hi — we were quoted 4 weeks but our launch is the 20th. Can you do it faster, and what would it cost?

Sidekick proposes

Thanks for flagging the date — the 20th is workable. We can compress to three weeks by running the integration in parallel with QA, which adds one engineer for that fortnight. I'll send a revised quote this afternoon so you can approve before we start.

Illustration · not a live extension

Also in the extension

OCR for screenshots and PDFs
Chart reading and data extraction
Page digests
Screen recording to GIF
Smart bookmark filing
MCP server for VS Code and Cursor

The same practice that builds these runs client projects — see what we do for other people.

02What we do

Two disciplines, deliberately kept together.

Most studios do the model or the interface. Splitting them across two vendors is where projects quietly die — in the handover. We hold both ends.

01AI/ML & Data

Intelligence that survives production

Models are the easy part. We build the evaluation, data plumbing, and monitoring around them so the system still works on the ten-thousandth request.

01.1
Machine Learning Systems
Forecasting, recommendations, classification, and anomaly detection — trained on your data, benchmarked against the baseline you already have.
We start by proving a model beats the heuristic you are running today. From there: feature pipelines, reproducible training, versioned artefacts, and a rollout you can reverse in one command.
- Baseline & feasibility study
- Feature pipeline
- Model training & registry
- Drift monitoring
01.2
LLM & GenAI Applications
Retrieval-augmented assistants, document extraction, and agentic workflows built with evaluation harnesses from day one.
Retrieval design, prompt and context engineering, tool-calling, and guardrails — with an offline eval suite so a prompt change never silently regresses quality in front of your users.
- Retrieval architecture
- Evaluation harness
- Guardrails & fallbacks
- Cost & latency budget
01.3
Data Engineering
Ingestion, warehousing, and transformation pipelines that are tested, documented, and cheap to run.
Batch and streaming ingestion into a warehouse or lakehouse, modelled with clear contracts and tested at every layer, so analytics and ML draw from one definition of the truth.
- Ingestion pipelines
- Warehouse modelling
- Data quality tests
- Orchestration & alerting
01.4
Analytics & Decision Support
Metric layers and dashboards that answer the question behind the question — built for the people who act on them.
We define metrics once, in code, then surface them where decisions are actually made: an internal tool, a Slack digest, an executive view. Fewer dashboards, more answers.
- Metric definitions
- Warehouse-native dashboards
- Self-serve exploration
- Alerting
01.5
MLOps & Platform
The unglamorous layer: CI for models, reproducible environments, observability, and rollback.
Training and inference wired into CI/CD, artefacts versioned alongside data, latency and quality tracked in the same place as your application metrics.
- Training CI/CD
- Inference serving
- Experiment tracking
- Observability

02Web & Mobile Products

Software people are glad to open

An intelligent system is worth nothing behind a bad interface. We design and ship the product layer — web, mobile, and the APIs underneath.

02.1
Web Applications
Fast, accessible, server-rendered applications — dashboards, portals, marketplaces, and internal tools.
TypeScript end to end, rendered on the server where it helps, measured against Core Web Vitals rather than opinions. Accessibility is part of the definition of done.
- Product design
- Next.js application
- Design system
- Performance budget
02.2
Mobile Applications
iOS and Android from one codebase, with offline behaviour and release pipelines handled properly.
React Native and Expo, with over-the-air updates, crash reporting, and store submission automated. Offline-first where field conditions demand it.
- iOS & Android builds
- Offline sync
- OTA release pipeline
- Store submission
02.3
APIs & Backend Platform
Typed APIs, background jobs, authentication, and integrations that hold up under real traffic.
Schema-first APIs with generated clients, background processing with retries you can inspect, and integrations that fail loudly rather than silently.
- API design & docs
- Auth & permissions
- Job processing
- Third-party integrations
02.4
Cloud & Reliability
Infrastructure as code, sensible environments, and the alerting that lets you sleep.
Reproducible infrastructure, preview environments per pull request, and observability wired in before launch rather than after the first incident.
- Infrastructure as code
- CI/CD pipelines
- Monitoring & alerting
- Cost review
02.5
Product Discovery & MVP
A focused build that puts something real in front of users while the question is still cheap to answer.
Scoped to the riskiest assumption, not the longest wishlist. You get a working product, the evidence to decide what is next, and a codebase worth continuing from.
- Scope workshop
- Clickable prototype
- Working MVP
- Launch & measurement plan

03Selected work

Two systems in production, in domains that punish guesswork.

A components manufacturer whose every enquiry is an engineering problem, and a distribution platform where being wrong costs a delivery. Both run the same shape: domain expertise first, model second.

01Power electronics manufacturingClient engagement

Genius Transformers

Advanced electromagnetic components, quoted at the speed of the enquiry

geniustransformers.com

The problem: Every enquiry arrives the same way: an inductance value, a current rating, and a mechanical envelope. Turning that into a matched part — or a new winding design, costed — is engineering judgement applied to a queue, and the queue is what caps how fast quotes go out.
What we built: A product catalogue spanning eight component families down to individual part numbers, and behind it the engineering layer that turns an incoming specification into a matched part and a price.

The intelligence layer

01.1Spec-to-part matching
Inductance, current rating, and mechanical envelope in; matching part numbers, or a proposed custom winding, out.
01.2Automated quoting and costing
Bill of materials, winding effort, and price estimated from the specification, so turnaround does not depend on who is at their desk.
01.3Demand forecasting
Order volume predicted per product family, so winding capacity and material purchasing are planned against expected demand rather than last month.
01.4Datasheet and RFQ extraction
Customer drawings, datasheets, and RFQs parsed into structured specifications instead of being retyped by hand.

Next.js
TypeScript
Python
Structured extraction
Forecasting

02FMCG distributionOur own product

DistributeIQ

Intelligence-driven distribution, from order to delivery to settlement

distributeiq.com

The problem: FMCG distribution runs on a chain nobody owns end to end. Orders live in one place, vehicles in another, claims and settlement somewhere else entirely — so the questions that matter, like which outlet is quietly declining, are answered late or not at all.
What we built: A distribution management platform covering the whole chain in one system — ordering, dispatch, delivery, claims, and settlement — with an intelligence layer reading the data the chain produces.

The intelligence layer

02.1Demand forecasting and replenishment
SKU-level demand predicted per distributor and outlet, driving reorder suggestions rather than a rep guessing from memory.
02.2Route and delivery optimisation
Drops sequenced and loads assigned against real vehicle capacity, delivery windows, and travel time.
02.3Settlement and claims anomaly detection
Suspect claims, damaged-goods returns, and reconciliation mismatches surfaced at settlement instead of at audit.
02.4Outlet scoring and next-best-action
Outlets ranked by growth potential, with the next action surfaced to the field rep standing in front of them.

Next.js
React Native
Python
Postgres
Forecasting

04How we work

Five stages.Each ends in something you can hold.

No open-ended retainers, no invisible months. Each stage has a named output and a point at which you can stop, redirect, or carry on.

01
Frame the problem
A short, paid discovery. We map the decision the software has to improve, audit the data you already hold, and write down what success looks like in numbers.
You receiveProblem brief, data audit, success metric
02
Prove the risky part
Before anyone designs a screen, we test the assumption most likely to sink the project — model feasibility, data quality, or an integration nobody has read the docs for.
You receiveSpike, benchmark, go / no-go
03
Design and build in the open
Two-week increments against a visible board. Every increment ends with something running in a shared environment, not a status update.
You receiveWorking increments, weekly demo
04
Harden and launch
Load, security, and accessibility checks. Observability and rollback before traffic. Documentation and a handover your team can actually work from.
You receiveProduction launch, runbook, handover
05
Measure and iterate
We watch the metric we agreed in step one. Models drift, users surprise you, and the roadmap should answer to evidence rather than to the original plan.
You receiveMetric review, iteration plan

05How we behave

Four commitments, not four adjectives.

Evidence before build

Every engagement opens by testing the assumption that would waste the most money if it were wrong. Sometimes the honest answer is that you do not need the model.

You own everything

Your repositories, your cloud accounts, your data. No proprietary runtime, no lock-in, no licence that turns into rent.

Senior engineers only

The people in the kick-off are the people writing the code. Nothing is quietly handed to a junior bench once the contract is signed.

Boring where it counts

Novel where the problem demands it, conventional everywhere else. Your team should be able to read this codebase without a tour guide.

06Who you work with

A studio, not a switchboard.

Arti Shinde

Founder & CEO

LinkedIn arti.shinde@thestackhub.com

Arti founded The Stack Hub on a straightforward conviction: most AI projects fail somewhere other than the model — in the data feeding it, or the product wrapped around it. The studio is built to hold both ends of that problem rather than hand one of them to someone else.

Every engagement starts with a conversation with Arti, not a sales team.

Based in: Pune, India
Founded: 2026
Replies: within one business day

07Before you ask

The questions that actually arrive.

If yours is not here, it is a short email away.

Do we need a data team before we can work with you?

No. Most engagements start with messy data in a handful of systems. Part of the first phase is auditing what you hold, what it is worth, and what has to be fixed before a model is even worth attempting.

How long does a typical project take?

Discovery runs one to two weeks. A focused MVP or a first production model is usually six to twelve weeks. Larger platform work runs in two-week increments with a demo at the end of each one, so you can stop or redirect at any boundary.

Can you work alongside our existing engineering team?

Frequently. We embed in your repositories and rituals, take a defined slice of the system, and leave documentation and tests behind so your team can carry it forward without us.

Who owns the code and the models?

You do, in full, from the first commit. We work in your version control and your cloud accounts wherever possible. There is no proprietary runtime you have to keep paying for.

Do you offer support after launch?

Yes — either a fixed monthly retainer covering monitoring, drift review, and iteration, or a clean handover with documentation and a runbook if you would rather own it internally.

What does an engagement cost?

Discovery is a fixed fee. Delivery is priced per two-week increment so the cost is predictable and you can stop at any boundary. We give a written estimate after the first conversation, at no charge.

08 — Next step

Tell us what is broken.We will tell you if we can fix it.

One conversation, no deck. If a model is not the answer, we will say so — that recommendation is free and it has saved people a great deal of money.

Start a project Partner with us

Or email arti.shinde@thestackhub.com

AI that ships.Not AI that demos.

Fixed-fee discovery. Two-week delivery increments. You own every commit.

AI that ships.Not AI that demos.

Two agentsunder one roof.

Framesleuth

What it runs on

Framesleuth Sidekick

Also in the extension

Two disciplines, deliberately kept together.

Intelligence that survives production

Machine Learning Systems

LLM & GenAI Applications

Data Engineering

Analytics & Decision Support

MLOps & Platform

Software people are glad to open

Web Applications

Mobile Applications

APIs & Backend Platform

Cloud & Reliability

Product Discovery & MVP

Two systems in production, in domains that punish guesswork.

Genius Transformers

The intelligence layer

DistributeIQ

The intelligence layer

Five stages.Each ends in something you can hold.

Frame the problem

Prove the risky part

Design and build in the open

Harden and launch

Measure and iterate

Four commitments, not four adjectives.

Evidence before build

You own everything

Senior engineers only

Boring where it counts

A studio, not a switchboard.

Arti Shinde

The questions that actually arrive.

Do we need a data team before we can work with you?

How long does a typical project take?

Can you work alongside our existing engineering team?

Who owns the code and the models?

Do you offer support after launch?

What does an engagement cost?

Tell us what is broken.We will tell you if we can fix it.

AI that ships.Not AI that demos.

Two agentsunder one roof.

Framesleuth

What it runs on

Framesleuth Sidekick

Also in the extension

Two disciplines, deliberately kept together.

Intelligence that survives production

Machine Learning Systems

LLM & GenAI Applications

Data Engineering

Analytics & Decision Support

MLOps & Platform

Software people are glad to open

Web Applications

Mobile Applications

APIs & Backend Platform

Cloud & Reliability

Product Discovery & MVP

Two systems in production, in domains that punish guesswork.

Genius Transformers

The intelligence layer

DistributeIQ

The intelligence layer

Five stages.Each ends in something you can hold.

Frame the problem

Prove the risky part

Design and build in the open

Harden and launch

Measure and iterate

Four commitments, not four adjectives.

Evidence before build

You own everything

Senior engineers only

Boring where it counts