An AI company
in India.

Every industrial operation keeps a material master: the list of every motor, bearing, valve, spare part, raw material and consumable it buys, stores and tracks in its ERP. Built up over decades by different people in different ways, it fills with duplicates, vague descriptions, missing manufacturers and specifications, and inconsistent units and classifications.

MIDAS reads those messy, real-world descriptions and turns them into clean, structured, standards-aligned records. Each record passes through a layered pipeline — the cheapest and most reliable methods first — and every result carries a confidence score and a source attribution: which layer made the call, and why. Nothing is a silent guess.

On top of that intelligence sits the full material lifecycle: search-first creation that checks for an existing part before a new one can be made, structured technical and commercial review, controlled changes and extensions, and integration-ready export. MIDAS runs on your own infrastructure with local language models by default; external AI services are an explicit, opt-in choice, never a requirement.

Finance teams rely on periodic reconciliation and sample-based audits, so most transactions are never looked at. Duplicate invoices slip through under a different vendor code or a slightly different number. The same vendor is paid inconsistently across entities. Spend drifts, and transactions land just under approval thresholds — and nobody notices until the problem is large.

ARGUS is built to change that from “audit a sample after the fact” to “watch everything as it happens.” It applies the same layered approach as MIDAS to transactional data: rules for known duplicate and control-evasion patterns, a model of what normal spend looks like for each vendor, cost centre and entity, and AI reasoning that explains each flag in plain language and suggests what to check next.

Every flag carries a confidence level and a clear explanation, so the team is not buried in false positives, and every flag, decision and resolution is logged for internal and external audit.

In multi-site organisations the same or interchangeable items are bought separately by different plants, at different prices, from different suppliers. Stock that is not moving ties up capital in one warehouse while another site raises a new purchase order. Equivalent parts sit under different codes, each treated as unique.

ATLAS is built to maintain one live intelligence layer over procurement and inventory. It resolves the same and interchangeable items across plant systems and suppliers into a single view — building on the materials foundation MIDAS establishes when the two run together — then tracks prices across suppliers, sites and time, and finds excess, dead and slow-moving stock across the network.

Its AI reasoning answers what-if questions, such as what consolidating a category to fewer suppliers would mean, and explains each recommendation in plain language for the procurement team.

Much of what an enterprise knows is written down but effectively invisible: contracts in one system, drawings in another, policies in a third. Renewal dates and notice periods sit buried in contract text until an auto-renewal triggers. People who knew where things were, and why decisions were made, move on.

MNEMOS is built to make that body of documents one connected source of knowledge. It extracts meaning from unstructured documents whatever system they live in, lets people search in plain language by intent rather than exact keywords, and tracks the obligations inside them — dates, renewal terms, notice periods and compliance requirements — so they surface before they are missed.

Ask a question such as which policies govern data retention for a business unit, and MNEMOS answers with citations to the specific documents behind it. It links related documents — a contract, its amendments and the correspondence around it — and knows which version of a drawing or policy is current.