Alot Labs
Applied AI engineering · embedded co-development

Enterprise AI Architecture. Production-Grade Systems.

We design, secure, and integrate custom multi-agent workflows, advanced retrieval-augmented generation (RAG) pipelines, and intelligent automation layers. We build the infrastructure that turns frontier models into deterministic business assets.

Schedule a Technical Architecture Briefing
See the deployments
3
deployments in production
5x
faster deployment than a traditional agency
18
systems built end to end
6
weeks to first production release
Deployments

Systems we ran ourselves before deploying them beside you.

All three deployments →
Deployment 01 · Swarm UI

Multi-Agent Orchestration Layer

A production-ready framework for executing and monitoring autonomous multi-agent networks, with real-time graph visualization of agent reasoning paths.

Deployment 02 · AutotestAI

Deterministic QA & Self-Healing Test Infrastructure

A cross-model consensus engine that removes manual quality assurance and application testing bottlenecks.

Deployment 03 · Rudy AI

Multimodal Semantic Routing & Cognitive Search

A high-throughput multi-agent indexing and retrieval engine for complex, unstructured data matrixes.

Core capabilities

Three practices, one architecture standard.

Capabilities and services →
Advanced Knowledge Retrieval (Enterprise RAG)

We engineer retrieval-augmented generation systems that turn fragmented, siloed corporate knowledge into secure, accessible assets.

Edge Intelligence & Custom Machine Learning

We bridge the gap between traditional data science, physical models, and generative AI.

Enterprise Security, Governance & Cloud Infrastructure

We design with a "zero-trust" philosophy to ensure your data remains proprietary and compliant.

How we work

We would rather tell you a programme is not worth running than bill you for a year of discovery. Every engagement ends with something in production, documented, and owned by you.

Read the process →

Bring us the workflow that is slow, expensive, or done by hand.

A technical architecture briefing runs 45 minutes with your engineers in the room. If we are not the right firm, we will say so.

Schedule a briefing
Deployments

Three systems we ran ourselves, then deployed inside client infrastructure.

Every engagement starts from working infrastructure rather than a blank repository, which is where the 5x comes from. Each deployment below is production code we have already debugged at our own expense, and each one lands inside your cloud with our engineers alongside your team.

PLANNER AGENT AGENT AGENT VERIFIED OUTPUT
An orchestration layer: a planner decomposes the task, agents work in parallel, and nothing reaches the output until it has been verified.
Deployment 01

Swarm UI

Multi-agent orchestration layer
Claude Sonnet Cytoscape topology SSE
Architecture

A production-ready framework for executing and monitoring autonomous multi-agent networks. Powered by Claude Sonnet, it features real-time graph visualization (via Cytoscape topology) allowing enterprises to audit agent reasoning paths dynamically.

Deterministic Governance

Includes user-in-the-loop "plan-confirm gates" before executing API calls or token spend.

Automated Source Verification

Built-in verification modules that pull and cross-reference all cited URLs, eliminating hallucinations before final delivery.

Asynchronous Processing

Server-Sent Events (SSE) streaming architecture for massive concurrent operations.

In production

Every cited source is re-fetched before a claim ships, and a reviewer flags fabrications at three severity levels. A completed run collapses into an auditable artefact: the reasoning graph, a written report, and the raw JSON.

What we would deploy for you

The orchestration layer runs inside your cloud, wrapped around your own tools and data sources. Your team gets the graph view for audit, and the confirm gates are wired to whoever owns the budget.

Deployment 02

AutotestAI

Deterministic QA and self-healing test infrastructure
Cross-model consensus DOM self-healing
Architecture

An intelligent, cross-model consensus engine designed to eliminate manual quality assurance and application testing bottlenecks.

Self-Healing Selectors

Automatically recalculates broken DOM selectors and repairs enterprise application test suites autonomously.

Multi-Model Assertion Consensus

Uses cross-model validation (Claude and secondary frontier models) to verify software state, drastically reducing false positives in production environments.

In production

Suites are generated from a one-line prompt, verified selectors persist across runs, and the platform monitors between releases rather than waiting for a human to press run.

What we would deploy for you

Pointed at the application your release train is actually blocked on, running in your CI, with the assertion consensus tuned to the false-positive tolerance your team can live with.

Deployment 03

Rudy AI

Multimodal semantic routing and cognitive search
FAISS Stateless orchestration Multimodal
Architecture

A high-throughput, multi-agent indexing and retrieval engine designed to process complex, unstructured data matrixes.

Cross-Modal Vector Ingestion

Fuses visual, audio, and textual signals into centralized FAISS embeddings for advanced natural language video and document intelligence.

Stateless Execution Guardrails

Orchestrated purely via database state and cron operations rather than brittle in-memory loops, ensuring 99.9% runtime reliability across enterprise databases.

In production

Visual, audio and textual signals are fused into a single embedding space, and orchestration runs on database state and cron rather than in-memory loops — so a failed step resumes instead of restarting the whole run.

What we would deploy for you

Indexing across the archives your people currently search by opening files one at a time, with the retrieval layer inside your VPC and the embeddings on your own storage.

Want to see a deployment running?

In a briefing we screen-share the deployment closest to your problem and walk through the architecture, the cost profile, and what broke the first time.

Request a walkthrough
Core capabilities & services

Three engineering practices, and six ways to deploy them.

The practices describe what we are good at. The services describe how you buy it. Most clients need one practice deeply and one adjacent to it, which is a scoping conversation rather than a menu decision.

Advanced Knowledge Retrieval (Enterprise RAG)
Typical deliverable: an internal answer layer over your document estate, with citations back to the source page and access that respects existing permissions.

We engineer retrieval-augmented generation systems that turn fragmented, siloed corporate knowledge into secure, accessible assets.

Dynamic Data Pipelines

Chunking, parsing, and cleaning structured and unstructured data (PDFs, media, APIs) for long-context model ingestion.

Hybrid Search Infrastructures

Deploying scalable vector databases (PostgreSQL/pgvector, Pinecone) with advanced lexical/semantic reranking mechanisms.

Edge Intelligence & Custom Machine Learning
Typical deliverable: a task-specific model that runs at the edge of your operation, where hosted inference is too slow or too expensive to call per event.

We bridge the gap between traditional data science, physical models, and generative AI.

Physics-Informed Neural Networks (PINNs)

Integrating constitutive neural networks (PyTorch) with deep analytical modeling for specialized industrial and engineering use cases.

Real-time DSP & Signal Processing

Designing lightweight, dependency-free audio and visual classifiers utilizing Web Audio APIs and digital signal processing for low-latency operational environments.

Enterprise Security, Governance & Cloud Infrastructure
Typical deliverable: the same system, running entirely inside your VPC, with the evidence your security review will ask for.

We design with a "zero-trust" philosophy to ensure your data remains proprietary and compliant.

Hyper-scaler Compatibility

Fully architected for native orchestration within client virtual private clouds (VPCs) via Amazon Bedrock (AWS) and Google Cloud Vertex AI.

Compliance Guardrails

Implementing rigorous middleware for data masking, PII protection, and zero-data-retention API configurations to meet strict SOC 2 and compliance baselines.

Services

Six ways to put engineers on the problem.

Deterministic system infrastructure

A defined scope, built and released inside your cloud, with source, architecture docs and runbooks you own outright.

Multi-agent orchestration layers

The repetitive judgment work in your operation, handed to agents that log every run and stop for a human when confidence drops.

Strategy and architecture roadmap

An honest read on where AI earns its cost, where it will quietly create work, and what to build first. Often the cheapest engagement we sell.

Embedded co-development

Our engineers inside your team, in your standups and your codebase, on a fixed number of days a week.

Training and enablement

Workshops built on your own workflows and data, so people leave having automated one real thing from their week.

Managed AI systems

We keep what we built healthy: model changes, cost, drift, and the on-call when something breaks.

Schedule a briefing
Pricing and engagement shapes →
Process

Five stages, and an exit at every one.

You should be able to stop after any stage and still be better off than when you started. That is the whole design of this.

01

Technical architecture briefing

Forty-five minutes with your engineers and the person who owns the problem. We are working out whether there is a real system to build and whether we are the right firm for it. No charge, no deck.

45 minutes · free
02

Read the systems and the work

We sit with the people doing the task, read the systems it touches, and count what it actually costs today. Most of what we learn contradicts the brief, which is the point. You end with a written scope, an estimate, and a recommendation that is sometimes to do nothing.

1–2 weeks
03

Build narrow, on your infrastructure

One workflow, real data, inside your VPC, with the failure cases handled rather than hidden. Working software every week from week one. If the numbers do not hold up we stop here, and you keep everything built so far.

3–6 weeks
04

Hand over properly

Source, architecture documentation, runbook, and sessions with the team who will own it. Handover is a deliverable with a date on it, not a goodbye email. If your engineers cannot change the thing themselves, we have not finished.

1 week
05

Stay useful, optionally

Some clients keep us on for monitoring, cost review and the next workflow. Others take it in-house and we hear from them a year later. Both are fine, and the contract does not push you toward either.

Monthly, cancel anytime
You own everything

Code, prompts, data and documentation are yours from the first commit, on your accounts and inside your cloud.

Weekly working software

Not status reports. Something your team can run, every week, from the first one.

We will talk you out of it

If a query and a rule solve it, that is our recommendation, and we will help you write the rule.
Pricing

Four ways to put a team on it.

Figures below are starting points for a single deployment. Final scope moves the number, and we confirm it in writing after the briefing rather than after the first invoice.

Orientation

Architecture roadmap

2 weeks · fixed fee
from $18,000

Interviews, a read of the systems, and a written roadmap: what to build, in what order, with estimates and the things we would not attempt. Credited against a build if you go ahead with us.

Most common

Fixed-scope deployment

6–10 weeks · fixed fee, staged
from $75,000

One system, specified up front and delivered into production inside your cloud, with handover included rather than sold separately. Payment is staged against releases, so you are never far ahead of what exists.

Alongside your team

Embedded co-development

2–3 days a week · monthly
from $32,000 / month

Engineers inside your team on a standing schedule, building and teaching at the same time. Right when scope will keep changing and you want the capability to stay in the building.

After launch

Run and improve

Monthly · cancel anytime
from $6,500 / month

Monitoring, model and cost review, small improvements, and someone to call when something breaks. Only offered on systems we built or have read end to end.

Worth knowing

Model and infrastructure costs are yours and run on your accounts, inside your VPC, so you can see them and take them with you.

We do not mark up licences or resell anyone's platform, which means the recommendation is not quietly a commission.

Anything we build for you stays yours. Our deployments come in as a starting point, not as a licence you have to keep paying for.

Questions we get asked
Our data cannot leave our cloud.

It does not. We deploy inside your VPC on Bedrock or Vertex AI, with zero-data-retention configurations and PII masking in the middleware. Nothing routes through infrastructure we own.

How is this different from a large consultancy?

The people in the briefing write the code, and the first deliverable is software rather than an assessment. We also arrive with working infrastructure instead of billing you to build it from scratch.

What happens when the model is deprecated?

Model calls sit behind an interface, so swapping a provider is a configuration change and a re-run of the evaluation set, not a rebuild. On a run-and-improve retainer we handle the swap before you notice.

Can our own engineers maintain it?

That is the test we hold ourselves to. Handover includes the source, the architecture documentation, the runbook and working sessions with the team who will own it. If they cannot change it themselves, the engagement is not finished.

Who owns the IP?

You do, from the first commit, in your repository. Our deployments come in under a licence that survives the engagement at no ongoing cost, and anything built on top of them is yours outright.

How fast can you start?

A roadmap can usually start within two weeks of the briefing, and a build within four. Security review is normally the long pole, so we start that paperwork in parallel rather than after signature.

Ask for a quote
About

Engineers who ship the systems they design.

We are engineers who kept being handed AI strategy decks and asked to make them real. The firm exists because the interesting work is the part after the pilot, and almost nobody wants to do it.

The people in your briefing are the people who write the code. Delivery teams are named and staffed per engagement across architecture, data engineering, compliance and front-end, with a partner network for specialist domain work. You are told who is on your project and why.

Our deployments come out of building eighteen systems end to end, seventeen of them still running. That is where the 5x comes from: we have already paid the debugging cost on the hard parts, at our own expense rather than on your invoice.

The range is deliberate: multi-agent research and support systems, real-time signal classifiers, a physics-informed network reproduced from a published paper, datasets of a thousand-plus records assembled and mapped, media pipelines running on open weights. Each one taught us where a class of system breaks before a client had to find out.

How we operate
Senior on delivery

No pyramid. No junior bench learning on your budget.

Named delivery team

Fixed for the engagement, so weekly releases stay real.

Your cloud, your data

Zero-data-retention configurations by default, inside your VPC.

Written, not implied

Scope, estimate, architecture and handover all exist as documents.
Contact

Schedule a Technical Architecture Briefing.

Forty-five minutes, your engineers included, no deck. Send a paragraph on the workflow and we will come back with the two or three questions that decide whether it is worth building.

Replies inside one business day. NDAs and security questionnaires welcome up front.
What the 45 minutes covers
0–10
The workflow as it runs today

Who touches it, how often, and where the time and money actually go.

10–25
Architecture on the whiteboard

How we would build it inside your cloud, which parts stay deterministic, and where a model is genuinely required.

25–35
A deployment on screen

We share the deployment closest to your problem and walk through the cost profile and what broke the first time.

35–45
Scope, timeline, and a verdict

A rough shape and figure, or the reason we think you should not build it. Both happen.

Useful in a first email
The workflow

What happens today, who does it, and how often.

The constraint

Compliance, cloud, data residency, or a deadline.

The stack

Where your data lives and which cloud you are on.

The trigger

Why now, and what happens if nothing changes.

Email
admin@alotlabs.com

One business day, from someone who will be on the call.

Who should be there

The person who owns the problem, and one engineer who knows where the data lives. Four people maximum; it stops being a working session after that.

What it costs

Nothing, and there is no follow-up sequence. If there is no system worth building we say so in the call and leave you the architecture notes.

Where we work

Remote by default, on your cloud and in your repository. On site for discovery and handover weeks where it earns the travel.