Enterprise software that acts, not just records.

MixinFire builds AI-native enterprise software: AI agents, agentic workflows, knowledge assistants and data platforms, plus our own AI-native ERP. Agents plan, act and ask for approval inside your systems of record.

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For business and technology leaders at mid-size and enterprise companies, in any industry.

Agent run Illustrative

  1. Signal Furnace F2 trips in the melt shop 14 batches expire within 60 days
  2. Plan Re-sequence rolling to protect dispatch dates Draft supplier returns and a replacement order
  3. Act Check heat chemistry; hold the off-grade lot Check supplier terms and stock across shops
  4. ApproveWaiting for you Planner approves the new sequence Pharmacist approves the order and returns
  5. Record Schedule, lot status and audit trail updated Order, return note and audit trail written

Every step is logged. Nothing changes without approval.

What we build

AI-native systems that do work inside your business, not demos beside it. Custom builds and our own products share the same governed agent core.

All solutions

AI agents and copilots

Agents that plan, call tools and act inside your business systems, with scoped permissions and human approval.

Agentic workflow automation

Multi-step processes such as approvals, document handling and reconciliation, run end to end by agents and checked by people.

Knowledge assistants

Assistants grounded in your documents and data through retrieval (RAG), with strict access control.

AI-native ERP

Our own ERP with agents built in, for steel and metals manufacturing and for medical distribution.

Data and cloud platforms

Streaming, warehouse, cloud and backend platforms that AI systems run on.

One platform underneath

Every solution runs on our reusable agent and workflow platform, extended for your systems.

Worked examples from two demanding industries.

The same agents and workflows adapt to any business. Pick an industry to see what they handle day to day; the color, copy and examples on this page follow your choice.

Steel and metals manufacturing

Heats, grades, yield and dispatch dates move together. Agents watch all four and act inside your ERP when something changes.

Explore the steel ERP

  • A furnace or mill goes down

    Re-sequence the rolling schedule to protect dispatch dates, then ask the planner to approve.

  • Heat chemistry is off grade

    Compare each heat’s chemistry with the grade specification and hold non-conforming lots before dispatch.

  • Scrap and yield drift

    Spot yield losses by shift, mill or grade and raise them with the numbers attached.

  • Test certificates

    Draft test certificates from heat and lab data, ready for QA sign-off.

Medical distribution and pharmacy

Stock sits in many shops, every batch has an expiry date and every invoice must be right. Agents keep watch and draft the paperwork.

Explore the medical ERP

  • Batches near expiry

    Find expiring stock across shops, draft supplier returns or transfers, and route them for approval.

  • Reorder at the right time

    Draft purchase orders from sales, stock on hand and supplier terms.

  • Stock across shops

    Suggest transfers between shops before one runs out and another overstocks.

  • GST invoicing

    Keep invoices, batch numbers and tax details consistent from purchase to sale.

Watch an agent handle a disruption, end to end.

A scripted run of the kind of task agents do inside the ERP. Each step is logged with its inputs and tool calls, and the change waits for a person to approve.

Illustrative simulation. All names, numbers and records are synthetic.

Furnace F2 trip: rolling schedule at risk

Illustrative simulation, synthetic data
  1. 06:42:10 Signal

    Induction furnace F2 tripped. Estimated restart in 3 h 20 min.

  2. 06:42:12 Agent

    Plan: re-sequence Rolling Mill 1 so heats from F1 roll first, keeping dispatch dates.

    schedule.resequence(mill="RM1", source="F1", keep_due_dates=true)
  3. 06:42:15 Tool

    New sequence keeps 11 of 12 dispatch slots; one slot moves by 2 h.

  4. 06:42:17 Agent

    Check heat H-0417 chemistry against grade Fe 500D.

    quality.check_chemistry(heat="H-0417", grade="Fe 500D")
  5. 06:42:18 Policy

    Carbon 0.27% exceeds the grade maximum of 0.25%. Lot L-0417-B is non-conforming.

  6. 06:42:20 Agent

    Hold lot L-0417-B, remove it from today’s dispatch and draft a re-test request.

    inventory.hold(lot="L-0417-B", reason="C above grade max")

Planner Approval needed: apply the new rolling sequence and hold lot L-0417-B?

Expiry sweep: three shops

Illustrative simulation, synthetic data
  1. 08:00:02 Signal

    Nightly expiry sweep finished across 3 shops.

  2. 08:00:05 Agent

    Find batches expiring within 60 days.

    inventory.find_expiring(days=60, shops="all")
  3. 08:00:07 Tool

    14 batches found; supplier terms allow returns on 5 of them.

  4. 08:00:09 Agent

    Draft return note RN-0094 for supplier S-12 and purchase order PO-1183 for replacements, based on 30-day sales.

    purchasing.draft_po(supplier="S-12", basis="sales_30d")
  5. 08:00:11 Policy

    The order includes prescription-only items: pharmacist sign-off required.

  6. 08:00:12 Agent

    Suggest moving 4 slow batches from Shop 2 to Shop 3, where they sell faster.

    inventory.suggest_transfer(from="Shop 2", to="Shop 3")

Pharmacist Approval needed: send PO-1183 and return note RN-0094?

Agents work through your data, your rules and your people.

Agents never touch raw tables directly. They work through a semantic layer and typed tools, and every change stops at a human approval before it reaches the system of record.

  1. Data sources

    ERP and business systems, documents, spreadsheets, sensors and event streams.

  2. Semantic layer

    Business definitions such as customer, order, batch or asset, mapped to your data, so agents query meaning, not raw tables.

  3. Agent orchestrator

    Plans multi-step work, picks the right tools and stops at policy limits.

  4. Tools / MCP

    Typed actions such as read, check, draft and update, exposed through APIs and the Model Context Protocol.

  5. Human approval

    People approve consequential actions inside the screens they already use.

  6. System of record

    Approved changes are written to your ERP or business system, with every step in the audit trail.

Autonomy you can govern, audit and switch off.

Enterprise AI fails on trust, not on demos. These controls are part of the design from the first workflow.

Audit record AR-2291 Illustrative
step        inventory.hold
agent       planner-assist
inputs      lot=L-0417-B, reason="C above max"
tool_calls  quality.check_chemistry, inventory.hold
approved_by planner (simulation)
outcome     applied
replay      available

  • Governed autonomy

    Human-in-the-loop approvals, scoped permissions and action limits. Agents draft and propose; consequential changes wait for a named approver.

  • Full audit trail

    Every agent step is logged with its inputs, tool calls and outcome, and can be replayed later.

  • Grounded answers

    Agents call tools over your own data through a semantic layer and use RAG over your SOPs, so they look things up instead of guessing.

  • Evaluated before shipping

    Agent workflows pass LLM evals and verification gates before they reach production.

  • Model-agnostic and interoperable

    Open APIs and the Model Context Protocol (MCP). Change models or connect other systems without vendor lock-in.

  • Deploy anywhere

    Run in the cloud, in your private VPC or on-premises. Your data stays with you.

  • Cost-aware

    Right-sized models per task, caching and bounded token spend per workflow.

Our flagship product: AI-native ERP

A full ERP built agent-first, for two industries where mistakes are expensive. Agents work on the same records your teams already use.

Steel and metals ERP

For mid-size steel plants and metal processors.

  • Heat and batch traceability
  • Grade and chemistry checks
  • Scrap and yield
  • Production planning and scheduling
  • Quality and test certificates
  • Dispatch
Explore the steel ERP

Medical and pharmacy ERP

For medical distributors and pharmacy chains.

  • Multi-shop inventory
  • Batch and expiry control
  • Purchasing
  • GST invoicing
  • Compliance records
  • Security-first, cloud or on-prem deployment
Explore the medical ERP

From assessment to agents in production.

A staged path, so you see value on one workflow before you commit to more.

  1. Assess

    We map your workflows, data and approval chains, then pick one or two agent use cases worth automating.

    You get: an assessment report and a pilot plan.

  2. Pilot

    We build one agent or workflow on your data, behind approvals, in your environment.

    You get: a working pilot with its audit trail and eval results.

  3. Roll out

    Extend to more workflows, teams and sites, connect existing systems and train your people.

    You get: agents in production across agreed workflows.

  4. Operate

    We monitor, evaluate and tune agents, review audit trails and keep token spend in budget.

    You get: ongoing operation.

Questions buyers ask us first.

Still unsure? Ask us directly at admin@mixinfire.com.

Do you build custom solutions or sell products?

Both. We build custom agents, agentic workflows and knowledge assistants on our reusable platform, and we sell our own AI-native ERP for steel and medical businesses.

How is our data kept secure? Can it run on-premises?

Your data stays in your environment: solutions can run in your cloud account, a private VPC or on your own servers. Agents act through scoped permissions, every step is logged, and consequential actions wait for human approval.

How do you stop the AI from making things up?

Agents do not answer from memory. They call tools over your own data through a semantic layer, retrieve your SOPs for procedures, and pass LLM evals and verification gates before release. Anything that changes a record waits for a person to approve it.

Does it work with our existing systems, such as SAP or Tally?

Agents connect through APIs and the Model Context Protocol (MCP), so they can read from and write to the systems you already run.

How long does a project take?

It starts with an assessment and a pilot on one workflow, then expands in stages.

What does it cost?

Book an assessment and we will share a proposal for your scope.

Which AI models do you use?

We are model-agnostic. We pick right-sized models per task, can use your preferred provider or self-hosted models, and use caching to keep token spend bounded.

Book an AI assessment

Tell us about one process that costs you time or money. We will show you what an agent or workflow would do with it, and what it would need from your data.

Book an assessment admin@mixinfire.com