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AI consultancy · Vertical industries

Deep AI integration for vertical industries

Little AI works side by side with enterprises and their domain experts to turn AI into production systems that create real value. We provide consulting, system design, system integration, forward deployed engineering and AI products.

Start a conversation → See our work

Engineering experience from Microsoft · Cruise Automation · Walmart · startups

  • Accounting
  • Legal
  • Geological Hazard Assessment
  • AI Infrastructure

What we do

From first idea to AI running in production

Five ways to work with us, from a few weeks of advice to a long-term embedded team.

Consulting

Find where AI actually pays off in your domain, and what it takes to get there.

  • Opportunity and feasibility assessment
  • Build / buy / adapt strategy
  • Roadmap with measurable outcomes

System Design

Architectures that are reliable, auditable and shaped by your domain's rules.

  • Agent and workflow architecture
  • Rule engines and decision tables where LLMs must not guess
  • Evaluation, provenance and compliance design

System Integration

Plug AI into the data, documents and tools your people already use.

  • Document, CAD and database pipelines
  • Cloud, multi-cloud and on-prem deployment
  • Identity, security and data residency

Forward Deployed Engineering New

Our engineers embed with your team and domain experts and ship into your real systems.

  • Work on your real data, constraints and users
  • Own delivery through to production
  • Transfer knowledge so your team runs it

AI Products

Vertical AI products built and operated together with domain experts.

  • Digital associates and report-generation agents
  • Workbenches for expert review and sign-off
  • Self-hosted or managed

How we work

We build with your experts, not around them

Domain AI succeeds when the people who know the work shape the system. So we do it together.

  1. Discover with experts

    Sit with practitioners to understand the work, the standards and where judgment really lives.

  2. Design for constraints

    Turn rules, compliance and data limits into architecture: what AI drafts, what rules decide, what humans approve.

  3. Build and integrate, embedded

    Forward deployed engineers build inside your environment and connect to the systems you already run.

  4. Deliver value and iterate

    Measure against real outcomes, expand scope step by step, and hand over a system your team owns.

Little AI co-builds with the enterprise and its domain experts Enterprisedata · systems Domainexperts Little AIengineers Value in production

Principles

How we make AI trustworthy in specialist work

Experts own the decisions

AI does the heavy lifting. Grades, judgments and sign-off stay with qualified people, backed by expert-verified rules.

Constrained, not free-form

Structure comes from standards and templates. Models are confined to the steps where they add value: extraction, rewriting, suggestions.

Traceable by default

Every output carries its sources and reasoning so it can be recomputed, audited and trusted.

Private when it must be

Open-source models on your own hardware or private cloud when data cannot leave your walls.

Selected work

AI that fits the way each profession works

A few of the domains we have gone deep in.

Geological hazard assessment

AI report generation with a verifiable core

Problem
Hazard-assessment reports must follow national standards, combine owner and field data, and ship with CAD drawings. Manual work is slow and hard to check.
Approach
A fact base shared by text and drawings; templates and rules for fixed content; LLMs only for extraction, rewriting and recommendations. Hazard grades come from expert-verified decision tables, never from a model, and are recomputed independently.
Delivered
A report-generation agent plus a web workbench to manage the report lifecycle, site data, Word documents and DWG drawings with in-browser preview.
PythonGoReactPostgreSQLCAD / DEMRAG

Legal

A digital associate for law firms

Problem
Law firms want AI help with research and drafting but cannot send client matters to public model APIs.
Approach
An agentic backend running entirely on locally deployed open-source models, with chat and research agents behind a streaming API.
Delivered
A self-hosted foundation for firm-specific assistants where client data never leaves the firm's environment.
vLLMQwen3.5-27BGoogle ADKFastAPIDocker / GPU

AI infrastructure

Multi-cloud ML platform for a computer-vision company

Problem
Training outgrew local machines. Terabyte-scale datasets, scarce GPUs in a single cloud and a mix of server and edge hardware slowed the team.
Approach
One job-submission layer over Kubernetes on two clouds plus on-prem GPUs, with Ray-based data pipelines, distributed training and compilation for edge targets.
Result
Training time cut from days to under 12 hours, thousands of managed jobs within weeks, and engineers moved to multi-node training without managing infrastructure.
SkyPilotKubernetesRayPyTorchTensorRTAnsible

engineering blog →

Accounting

AI for accounting workflows

Focus
Document-heavy, rule-bound accounting work where accuracy and an audit trail matter as much as speed.
Approach
The same pattern as our other domains: practitioners define the rules, AI handles extraction and drafting, and every figure stays traceable to its source.

Ask about this work →

< 12htraining time, down from days
4verticals: accounting, legal, geo-hazard, AI infra
Multi-cloudand on-prem GPU deployments
Openmodels, run on your own hardware

Team

Engineers who have shipped at scale

Our team brings production engineering experience from large technology companies and high-growth startups.

MicrosoftCruise AutomationWalmartStartups

Large-scale systems

Reliable, observable services that run for millions of users.

ML and autonomy infrastructure

Data, training and deployment pipelines for demanding ML workloads.

Startup speed

Small teams that ship fast and own outcomes.

FAQ

Common questions

What does Little AI do?

Little AI helps enterprises integrate AI deeply into vertical domains. We work together with the company and its domain experts, and provide consulting, system design, system integration, forward deployed engineering and AI products.

What is forward deployed engineering?

Our engineers embed with your team and domain experts, work against your real data and systems, own delivery through to production, and transfer knowledge so your team can run and extend the system.

Which industries do you work in?

Accounting, legal, geological hazard assessment and AI infrastructure so far. The approach applies to any domain with specialist knowledge, standards and documents.

Can AI run on our own infrastructure?

Yes. When data cannot leave your environment we deploy open-source models locally or in your private cloud, for example a law-firm assistant running on self-hosted models.

How does an engagement start?

Email us. We begin with a short discovery conversation with your team and domain experts, then agree on a focused first deliverable that proves value before scaling.

Do you build products or only consult?

Both. Consulting and engineering engagements are our core work, and we also build vertical AI products with domain experts.

Who is on the team?

Engineers with production experience from Microsoft, Cruise Automation, Walmart and several startups.

Let's put AI to work in your domain

Tell us about your industry and the problem you want to solve. We will reply with how we would approach it.

contact@little-ai.io →