Explainable by design
Welcome to Amatix AI. We are a deep-tech startup building the future of autonomous intelligence. We specialize in Explainable AI (XAI), Agentic Workflows, and Computer Vision to create smart, transparent, and scalable AI solutions for the enterprise.
Services
From interpretable models to autonomous agents, every system we deliver is built to show how it reached its output.
Make every model decision inspectable, so teams can trust it, challenge it and sign off on it.
Autonomous, multi-step agents that plan, use your tools and hand off to people at the right moment.
Turn cameras, images and scanned documents into structured, reliable signals.
Language models adapted to your domain, measured against your standards, deployed on your terms.
Multimodal models that read images, video and documents the way your experts do.
End-to-end automation of business operations, connected across the systems you already run.
Custom n8n workflows and integrations, self-hosted and maintainable by your team.
Intelligent automation for document, approval and data pipelines.
Answers grounded in your private knowledge, with sources cited every time.
Custom neural networks designed, trained and optimized for your data and constraints.
Explainable by design
Most AI systems give you an answer. Ours give you the answer along with the reasoning, the evidence and the confidence, so the people accountable for a decision can stand behind it.
Every decision our systems make is recorded as a trace that reviewers can read, question and replay.
InputWhat the system was asked to decide.
ReasoningWhich agents and tools acted, in order, and what each found.
SourcesThe evidence used, cited to the record.
ConfidenceHow sure the system is, and the factors behind that score.
OutputThe decision, and whether a person needs to approve it.
Input
Finance operations
An invoice bills 1,200 units, but the matching purchase order lists 1,000 units at the same unit price.
Reasoning
Extraction agentDocument parser
Read the supplier, PO reference, quantities, unit price and totals from the invoice.
Matching agentERP lookup
Retrieved the PO and goods receipt. The receipt confirms 1,000 units delivered.
Policy agentPolicy retrieval (RAG)
Found the tolerance rule: quantity variances above 2% require buyer approval.
VerifierConsistency check
Confirmed a 20% variance, with no partial delivery or amended PO on record.
Sources
Confidence
Output
Hold payment and route to the buyer for approval.
Human review · Human approval required: the variance exceeds policy tolerance.
How we work
A delivery process built around measurable outcomes, with governance designed in from the start.
We map the workflow, the data and the decisions involved, and agree on what good looks like, including what must be explainable and to whom.
We design the system: models, agents, tools, guardrails and the explanation layer, with human checkpoints placed where the stakes are.
We build iteratively against your real data. Evaluations run from day one, so progress is measured rather than assumed.
We deploy into your environment, whether cloud, private cloud or on-premises, integrated with the systems and permissions you already have.
We monitor accuracy, drift and cost in production, and keep every decision explainable for reviewers and auditors.
Solutions
Illustrative use cases by industry. Each one combines the capabilities above, and each one is explainable end to end.
Quality, uptime and know-how on the shop floor, with evidence behind every call.
Detect surface defects, assembly errors and missing components on the line, with heatmaps that show inspectors exactly what the model flagged.
Combine sensor data and maintenance logs to flag equipment at risk, and explain which readings drove each alert.
Give technicians an assistant over manuals and SOPs that cites the exact page and revision it used.
Fewer manual investigations and faster exceptions across carriers, hubs and paperwork.
Agents investigate delayed or damaged shipments across carrier, warehouse and ticket data, then propose a resolution for approval.
Extract and validate data from bills of lading, invoices and customs forms, and route low-confidence fields to a reviewer.
Track vehicles, pallets and dock occupancy from existing cameras to cut dwell time and congestion.
Decisions that stand up to model-risk review, audit and regulators' questions.
Credit, fraud or AML models where every score comes with the factors behind it, ready for model-risk review.
Match invoices, purchase orders and payments, and explain each exception before it reaches an analyst.
Answer questions over policies, filings and research notes, with cited sources and access controls.
Better shelves, cleaner catalogs and faster service, without losing the human touch.
Detect out-of-stocks and planogram gaps from store imagery, and prioritize fixes by impact.
Generate and validate product attributes from images and supplier data, with human review for low-confidence items.
Agents resolve order and returns queries using your systems, and escalate to staff with the full context attached.
Earlier signals and clearer incident records for physical and operational security.
Detect intrusions, loitering and unsafe behavior in real time, with the clip and reasoning attached to each alert.
Correlate badge, camera and system logs to surface the anomalies worth a human look.
Draft incident reports from events, footage notes and logs, with every statement linked to its source.
Platform architecture
Data flows through retrieval, vision and language models into agents and automation. The explainability and audit layer records every step.
01Data sources
02Intelligence layer
03Agent orchestration
04Automation
Explainability & audit layer
Spans every layer above.
Trust & governance
Enterprise AI has to earn trust from users, reviewers and auditors. We build the controls into the system itself.
Every model output and agent action can be traced to its inputs, steps and sources. Explanations are built into the system from the start.
Architectures designed for data minimization, encryption in transit and at rest, and retention rules agreed with you up front.
You decide where people review, approve or override. Agents escalate with full context instead of guessing.
Decisions, prompts, tool calls and model versions are logged so any outcome can be reconstructed and reviewed later.
Run in your own cloud account, a private cloud or on-premises, with options for restricted and offline environments.
Insights
Practical writing on explainability, agents and vision. The first articles are coming soon.
Feature attributions were built for single predictions, but agents take dozens of steps. How we think about traces, sources and accountability in multi-step systems.
A cited answer is only useful if the citation supports the claim. Notes on retrieval evaluation, attribution checks and reviewer workflows.
Lighting changes, new camera angles, rare defects. What it takes to keep vision models accurate and explainable after deployment.
Tell us about the workflow you want to automate. We'll show you what a transparent, auditable version could look like.
Contact
Tell us what you want to automate or make explainable. We'll reply with next steps and, if it's a fit, set up a tailored demo.