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ML Stack Labs
U.S. basedEngineering network across 11 time zones

Software teams that ship, for companies that can’t afford not to.

ML Stack Labs is a U.S. consultancy that builds and rescues software for regulated, high-stakes businesses. We scope honestly, staff with senior engineers, and hand the system back to your team when it is done.

  • Healthcare
  • Fintech
  • AI
  • Education
  • Agriculture
  • Automotive
clinical_agent.pyLangGraph
1from langgraph.graph import StateGraph
2from mlstack.evals import Gate
3
4# Refuse beats guessing in a clinical setting.
5graph = StateGraph(ClinicalState)
6graph.add_node("retrieve", retrieve_cited)
7graph.add_node("answer", answer_or_refuse)
8
9graph.add_conditional_edges(
10"retrieve",
11lambda s: "answer" if s.citations else "refuse",
12)
13
14app = graph.compile(checkpointer=store)
15Gate("groundedness", min=0.92).enforce(app)
evals passing640 casesgroundedness 0.94

From a clinical retrieval agent we shipped in 2024. The graph refuses to answer without citations, and an evaluation gate blocks the deploy if groundedness drops below 0.92.

Trusted by companies

Sully AI
Pony.ai
Meridian Health
Northlake Capital
Vantage Pay
Alder Diagnostics
Brightpath Learning
Cardinal Bancorp
Tillage Systems
Axle Motion
Halcyon Care
Ledgerline
Summit Scholars
Rowan Robotics
Sully AI
Pony.ai
Meridian Health
Northlake Capital
Vantage Pay
Alder Diagnostics
Brightpath Learning
Cardinal Bancorp
Tillage Systems
Axle Motion
Halcyon Care
Ledgerline
Summit Scholars
Rowan Robotics
60+
Products shipped
since 2019
94%
Client retention
past three years
11
Time zones
engineering network
12 days
Median time to staff
from signed SOW

What we do

Six ways companies bring us in

Most engagements start as one of these and grow into two. We are equally happy owning a whole product or one stubborn layer of it.

Service detail
01

AI and LLM Engineering

Retrieval, agents and copilots that hold up under real usage.

  • LangChain
  • LangGraph
  • OpenAI
  • Anthropic
  • pgvector
  • Pinecone

We build LLM features the way we build the rest of the product: versioned, tested and measured. That means an evaluation suite before launch, retrieval you can trace back to a source document, and cost and latency budgets agreed up front rather than discovered on the first invoice. Most of our work here runs on LangChain and LangGraph, with the orchestration layer kept thin enough to swap a model provider in an afternoon.

02

Product Engineering

Full-stack teams that take a product from scope to production.

  • TypeScript
  • React
  • Next.js
  • Node.js
  • Python
  • PostgreSQL

A pod of three to six engineers with a tech lead who stays on the project start to finish. We work in your repositories, in two-week increments, with a demo at the end of each one. You are never more than ten working days from something you can click on, and never dependent on a person you have not met.

03

Data and Platform

The layer everything else depends on, built to stay boring.

  • Airflow
  • dbt
  • Snowflake
  • Kafka
  • Terraform
  • Kubernetes

Ingestion, warehousing and the contracts that keep schema drift from quietly corrupting a downstream model. We also handle the infrastructure underneath: Terraform-managed environments, sensible observability, and deploys that a single engineer can run on a Friday without a war room.

04

Legacy Modernization

Getting an old system moving again without stopping the business.

  • .NET
  • Java
  • PHP
  • Python
  • Docker
  • PostgreSQL

Most of the systems we are called into cannot be switched off for a rewrite. We work incrementally. Carve out a bounded piece, put it behind a stable interface, replace it, then repeat, so value lands every increment and there is never a single terrifying cutover weekend. We have done this with monoliths well past a decade old, and with vendor systems nobody on staff had shipped to in years.

05

Team Extension

Vetted senior engineers embedded in your team, not a black box.

  • Time zone overlap
  • Direct Slack access
  • Your tooling
  • Your process

When you need capacity rather than a project, we place engineers from our network directly into your team, your standups and your code review. Every candidate has been through our own technical screen and a paid trial task before you see them. If a placement is not working in the first two weeks, we replace them and you are not billed for the overlap.

06

Discovery and Architecture

A two-week engagement that ends in a plan you could hand to anyone.

  • 2 weeks
  • Fixed price
  • Written deliverable
  • No obligation to continue

Before a large build we run a short paid discovery: we read the code, interview the people who maintain it, and come back with a written architecture, a risk register and a costed roadmap. Roughly a third of these end with us recommending a smaller project than the client came in asking for. That is a good outcome and we would rather find it in week two than month six.

Industries

Domains where the details are the whole job

Every one of these has rules that shape the architecture: a compliance regime, an interoperability standard, or a physical constraint. We have shipped inside all six.

Healthcare

01

EHR integration, clinical workflow and the compliance work around both.

We build on the systems clinics already run, reconciling records between vendors who model them differently, and designing for a HIPAA review from the first commit.

  • FHIR
  • HL7 v2
  • OpenEMR
  • HIPAA

9 EHR integrations shipped since 2021

Fintech

02

Payments, ledgers and the reconciliation nobody demos but everybody needs.

Card and ACH flows, ledgers that balance, and the reconciliation jobs that catch it when they do not. Most of this is regulated, so we plan for the audit up front.

  • Stripe
  • Adyen
  • Plaid
  • PCI-DSS

$0 in reconciliation breaks across 3 live ledgers

Artificial Intelligence

03

LLM features that survive contact with real users and real invoices.

We are asked for a chatbot and usually deliver something narrower and more useful, evaluated before launch and instrumented so you see quality and spend per feature.

  • LangChain
  • LangGraph
  • RAG
  • Agents

Median 41% cut in LLM spend after evaluation work

Education

04

Learning platforms, SIS integration and assessment at scale.

Course delivery plus the standards work that lets a platform talk to systems a district already runs, and the FERPA and accessibility work that keeps it compliant.

  • LTI 1.3
  • SCORM
  • xAPI
  • OneRoster

Platforms serving 120k+ enrolled learners

Agriculture

05

Field data, equipment telemetry and forecasting that works offline.

Agtech carries a constraint most software does not: the people using it are often out of signal. We build offline-first, for a device that reconnects all at once.

  • MQTT
  • LoRaWAN
  • GIS
  • NDVI

Field apps used across 340k acres

Automotive and Mobility

06

Perception tooling, fleet telemetry and the data loop behind both.

We work on the software around autonomous and connected vehicles: the data loop that turns a week of driving logs into a training set, and fleet telemetry above it.

  • ROS 2
  • Telematics
  • OTA
  • Perception

2.1PB of driving logs processed to date

How we work

Four stages, and you can stop after any of them

No twelve-month master agreement before anything is built. Each stage produces something you own outright, whether or not you continue.

Around a third of discovery engagements end with us recommending a smaller project than the client came in asking for. We would rather find that in week two than month six.

  1. 0145 minutes

    Scoping call

    You walk us through the problem and the constraints: budget, deadline, and the internal politics we should know about. We tell you honestly whether it is something we should take on.

    • Written summary
    • Rough size and shape
    • Go / no-go
  2. 022 weeks

    Discovery

    Paid and fixed-price. We read the code, talk to whoever maintains it, and produce an architecture, a risk register and a sequenced roadmap you own outright.

    • Architecture doc
    • Risk register
    • Costed roadmap
  3. 032-week increments

    Build

    A dedicated pod works in your repositories with a demo every second Friday. Scope is reviewed at each increment boundary, which is also where you can stop.

    • Working software
    • Demo each increment
    • Weekly written status
  4. 042 to 4 weeks

    Handover

    Runbooks, architecture decision records and paired on-call shifts until your team is running the system without us. We stay reachable afterwards on a support retainer if you want one.

    • Runbooks and ADRs
    • Paired on-call
    • Optional retainer

Selected work

Projects, constraints and what actually changed

Client names are held under NDA. The problems, the decisions and the numbers are not, and we will walk through any of these in detail on a call.

HealthcareMulti-site specialty clinic group

One patient record across eleven clinics on four different systems

The problem

Eleven acquired clinics ran four EHRs between them. Front-desk staff were rekeying demographics by hand, and duplicate records were causing scheduling errors serious enough to reach the board.

  • FHIR R4
  • HL7 v2
  • OpenEMR
  • Python
  • PostgreSQL
  • AWS
11 to 1
Patient record systems of truth
97.4%
Auto-match rate on migration
6.5 hrs
Front-desk hours saved per site, weekly
Duration
7 months
Team
5 engineers, 1 clinical analyst
FintechB2B payments platform, New York

Rebuilding a ledger that had drifted $180k out of balance

The problem

Balances were computed from a mutable transactions table with no audit history. Month-end close took nine days and finance had lost confidence in the numbers, which was becoming a problem in diligence.

  • Stripe
  • Node.js
  • TypeScript
  • PostgreSQL
  • Terraform
9 days to 4 hrs
Month-end close
$0
Unexplained variance since cutover
21 days
Parallel-run agreement before cutover
Duration
5 months
Team
4 engineers
AIHealthcare data company

A retrieval agent clinicians will actually put their name to

The problem

An internal LLM prototype answered clinical questions fluently and cited nothing. Clinical leadership would not approve it, and per-query cost was rising faster than usage.

  • LangGraph
  • LangChain
  • pgvector
  • Python
  • Anthropic
  • OpenAI
94%
Answers passing clinician review
58%
Reduction in cost per query
640
Labeled questions in the eval set
Duration
4 months
Team
3 engineers, 1 ML engineer

Engagement models

Three ways to work with us

Everything is billed monthly with no long-term lock-in. Published starting prices, so you can tell before a call whether we are in your range.

Discovery

From $12,000

Fixed price · 2 weeks

A short paid engagement that ends in a written plan. Roughly a third conclude with us recommending less work than the client came in for.

  • Codebase and architecture review
  • Stakeholder interviews
  • Risk register and mitigations
  • Costed, sequenced roadmap
  • Fixed-price proposal for the build

Best when the problem is not yet well defined.

Talk about discovery
Most common

Dedicated pod

From $28,000 / month

Monthly · 3 to 6 engineers

A cross-functional team with a tech lead who stays for the whole engagement. Scope is agreed and reviewed every two weeks.

  • Tech lead, engineers and QA
  • Demo every second Friday
  • Weekly written status
  • Work in your repos and cloud
  • Stop at any increment boundary

Best for a defined product or platform build.

Talk about dedicated pod

Team extension

From $9,500 / month

Monthly · per engineer

Individual senior engineers embedded in your existing team, your process and your code review. No project management layer in between.

  • Pre-screened and trial-tested
  • 4+ hours ET overlap guaranteed
  • Direct Slack and standup access
  • Two-week replacement guarantee
  • Scale up or down monthly

Best when you need capacity, not a project.

Talk about team extension

Prices are starting points for a typical engagement and are confirmed in writing after scoping. Discovery is credited against the build if you continue within 60 days.

In their words

What clients say when the project is over

They spent the first two weeks telling us the project we asked for was the wrong one, and were right. The version we actually built was smaller, cheaper and is still running.
VP EngineeringMulti-site specialty clinic groupName withheld under NDA
The ledger cutover was the least dramatic infrastructure change we have ever done. Twenty-one days of parallel running and then it was just done.
Chief Technology OfficerB2B payments platform, New YorkName withheld under NDA
We had been quoted a rewrite by two other firms. ML Stack Labs carved it up and shipped the first piece in five weeks without taking the system down.
Director of TechnologyK-12 learning platformName withheld under NDA

For engineers

We are always hiring into the network

Placements are with U.S. companies, one client at a time, on engagements that typically run past eleven months. Rates are stated before you accept.

Join the network

Senior Full-Stack Engineer

High

Product teams across healthcare, fintech and education clients.

  • TypeScript
  • React
  • Next.js
  • Node.js
  • PostgreSQL

AI / LLM Engineer

High

Retrieval systems, agent workflows and evaluation tooling.

  • Python
  • LangChain
  • LangGraph
  • Vector DBs
  • Evals

Backend Engineer

High

Payments, ledgers, integrations and high-volume APIs.

  • Go
  • Python
  • Node.js
  • PostgreSQL
  • Kafka

Data Engineer

Steady

Warehouse modeling, pipelines and data quality.

  • dbt
  • Airflow
  • Snowflake
  • Spark
  • SQL

DevOps / Platform Engineer

Steady

Infrastructure as code, CI/CD and cloud cost work.

  • Terraform
  • Kubernetes
  • AWS
  • GCP
  • Observability

Mobile Engineer

Steady

Offline-first field apps and consumer healthcare products.

  • React Native
  • Swift
  • Kotlin
  • SQLite

Questions

Before you get in touch

Not covered here? Email business@mlstacklabs.com and you will get a real answer from an engineer, usually the same day.

Tell us what is on fire.

Bring us a stalled project, a system nobody wants to touch, or a product you need built properly the first time. We will tell you honestly whether we are the right team for it.

  • 45 minutes, no cost, no deck
  • You speak to an engineer, not a salesperson
  • We say no if it is not a fit