AI Consultancy

We build the AI that actually ships.

Most AI work stalls between the demo and production. Guardian is the team you bring in when you need a pipeline running against real data, integrated into real systems, with numbers you can defend — whether that's automating an entire function or putting a control layer in front of autonomous agents.

Scope a project → See how we think

Pipelines

Ingestion to action, not notebooks

Agents

With identity, policy, and audit

Vision

Inspection and autonomous tracking

Federal

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01 — CORE PRACTICE

AI Pipelines & Automation

Automating a whole function, not adding a chatbot to it. We design the path from wherever your data actually lives — the SFTP dump, the scanned PDFs, the legacy replica — through retrieval and inference to something your business genuinely acts on.

Document-heavy processes — invoices, claims, contracts, shipping paperwork, applications.
Classification and routing at volume, with confidence-based exception handling.
Retrieval systems over your own corpus, with access control enforced at retrieval and citations in every answer.
Evaluation sets built with your subject-matter experts — the most durable asset you get from us.
Integration into the system where the work already happens, not a separate tool nobody opens.

Deep dives

What is an AI pipeline?9 min What is RAG?7 min How to measure whether AI works6 min Why AI pilots fail7 min Automating your sector →7 guides

Custom AI Development

When off-the-shelf does not fit — because the problem depends on your proprietary data, your taxonomy, or a physical process nobody else has. We build the model layer and, more importantly, the honest assessment of whether you need one.

Fine-tuned models for narrow high-volume tasks where a small model can replace an expensive one.
Structured extraction against schemas that have to be right every time.
Anomaly detection where defect or fraud examples are too rare to train on directly.
Model selection and cost engineering — routing, caching, and context design that routinely cuts spend 60–80%.
A straight answer on build versus buy, including when the answer is "don't build this."

Deep dives

RAG vs fine-tuning vs prompting7 min Build vs buy AI6 min Cutting inference cost7 min LLM API pricing compared7 min All cost guides →6 guides

AI Agents & Orchestration

Agents are the right tool when the path genuinely cannot be known in advance — and the wrong tool far more often than they are deployed. We build the ones that should exist, with the controls that make them safe to give write access to.

Tool design and hardening — validation, idempotency, and error messages a model can act on. Usually 30–40% of the real work.
Step budgets and spend caps, because an agent without them is an unbounded invoice.
Authorization and audit — the full human → agent → permission → action → outcome chain.
Behavioural evaluation for systems where you cannot assert exact outputs.
An honest pipeline-instead recommendation when you can already draw the flowchart.

Deep dives

What is an AI agent?8 min What it costs to build one7 min Agents with too much access7 min Guardian Agent Defense →product

AI Security & Governance

The controls that let an organization say yes to AI. Most of what passes for AI governance is documentation; the parts that matter are architectural decisions made in code — and they are the ones that fail audits.

Agent access control — inventory, scoping, and an enforcement point in front of consequential actions.
Retrieval access control — the permission bypass most RAG systems ship with by default.
Prompt injection review across every untrusted input reaching a model with tools.
Data flow assessment — what leaves your boundary, under what terms, and what your traces are quietly retaining.
Framework alignment — NIST AI RMF, ISO 42001, EU AI Act, and sector rules.

Deep dives

What is prompt injection?7 min How AI systems leak data7 min Protecting data from frontier labs7 min NIST AI RMF, ISO 42001, EU AI Act7 min All security guides →4 guides

Robotics & Computer Vision

Physical AI, where the engineering that decides success happens before any model runs. We have built inspection systems using industrial lasers and vision, and autonomous camera platforms that track subjects in real time — and the lesson from both is that lighting and fixturing beat architecture every time.

Automated visual inspection — defect detection, dimensional measurement, presence/absence at line speed.
Imaging design — lighting geometry, optics, and fixturing, which set the accuracy ceiling.
Anomaly detection for production lines where defects are too rare to train a classifier on.
Autonomous tracking — detection, re-identification, and motion control smooth enough to be watchable.
Edge deployment with drift monitoring on input statistics, not just output accuracy.

Deep dives

Machine vision inspection7 min Autonomous tracking systems6 min Automating manufacturing QA7 min All robotics guides →2 guides

Fractional Technical Leadership

Sometimes the gap is not a build — it is that nobody senior enough owns the technical direction. We embed as engineering leadership: setting architecture, running vendor selection, and making the calls that are expensive to get wrong.

Technical due diligence on AI vendors and acquisition targets.
Architecture review of systems already in flight, before they become expensive.
AI strategy that starts from which processes are worth automating, not from the model.
Team building — hiring plans, interview design, and capability transfer so you stop needing us.
Executive briefing that translates between engineering reality and board expectations.

Deep dives

What AI consulting costs7 min Calculating automation ROI7 min Why AI pilots fail7 min Automating recruiting7 min

How we work

Assessment first. Always.

We will not quote a large build against data we have not examined, because nobody can honestly predict AI performance on a corpus they have not seen. A short paid assessment is the cheapest risk reduction available in this entire category.

STEP 01

Assessment

We look at your real data, verify the access path actually works, and give you an achievable accuracy estimate. Two to four weeks.

STEP 02

Evaluation set

Your experts label a held-out set. This defines "done" numerically and becomes yours permanently.

STEP 03

Build

End to end — ingestion through to the action in your system. Your engineers in the codebase from week one.

STEP 04

Handover

Runbook, eval set, and a team that can run it without us. That is the point.

Tell us what you want automated.

We will tell you whether AI is the right tool, roughly what it costs, and what would have to be true for it to work. If the answer is that you should not build it, we will say so.

Talk to Us → Browse the Learning Center