/ functionality
Everything your integration stack should do —without the consulting bill.
Inferrex replaces the integration consultant, the middleware platform, and the maintenance team. Pick an area to go deeper.
/ at a glance
One platform, end to end.
Schema inference
Point at any API; the schema appears and fields classify themselves in real time.
Pipeline graphs
One source, many targets, one pipeline — with per-field sync tiers and actions on mappings.
Self-healing
APIs change; Inferrex detects, re-infers, and repairs — silently or with approval.
Intelligence
Waste, gaps, and pipeline suggestions surfaced with cost and ROI.
Backup & Recovery
Pre-write snapshots, point-in-time restore, tiered archival, deletion certificates.
Developer surface
InferrexMCP, InferrexSDK, InferrexCLI, and Copilot — drive everything from your IDE.
Nothing syncs until you approve it.
Point at any API. Watch it understand itself.
No YAML. No field-mapping wizards. No integration consultant. Connect an API and watch the schema appear — then watch fields classify themselves by business meaning, with confidence scores. Only genuinely ambiguous fields touch InferrexAI; your corrections improve future inference.
| Pipeline layer | What it does |
|---|---|
| Schema discovery | Detects the protocol and reads the published schema. Raw structure instantly. |
| Field classification | Classifies every field by business meaning — not just data type. Confidence scored. |
| Relationship inference | Identifies how entities relate. Builds a full entity graph. |
| Semantic interpretation | Attaches plain-English meaning to every entity and field. |
| Cross-system matching | Proposes field mappings between systems with confidence scores. |
| Consistency supervision | Reviews output for internal consistency before you see it. |
One source. Many targets. One pipeline.
Traditional tools force point-to-point pairs. Inferrex uses a graph: one CRM can feed accounting, support, and a warehouse at once — each connection with its own sync frequency, mappings, and actions. Actions attach to mappings and fire when data moves — no separate workflow engine.
| Sync mode | When |
|---|---|
| Realtime | <5s — stock prices, chat, order status |
| Near-realtime | <60s — CRM updates, deal-stage changes |
| Interval | 5m–24h — invoices, reports, inventory |
| On-change | Event-driven — when the source notifies |
| Batch | Daily/weekly — warehouse loads, compliance snapshots |
| Manual | On demand — migrations, reconciliation |
A bad record doesn't stop the sync — it gets pulled aside.
The old way a sync fails is all-or-nothing: one malformed record, one value that doesn't fit, and the whole batch errors out or, worse, corrupts what it touches. You're left rolling back a thousand good records because of one bad one.
Inferrex flags anomalies as they appear mid-sync and quarantines the offending records, letting the rest of the batch complete cleanly. The good data lands on time; the suspect data is set aside, intact, for you to inspect and resolve on your own schedule. One bad row no longer holds the other nine hundred and ninety-nine hostage — and you get a clear, isolated view of exactly what didn't fit and why.
When APIs break, Inferrex fixes itself.
Provider APIs change constantly — field renames, deprecations, auth changes. Traditional integration breaks silently. Inferrex watches every provider's docs, changelog, and SDKs, classifies the change, and resolves it.
| Healing tier | Behaviour |
|---|---|
| Tier 1 — silent fix | Retry, re-auth, schema re-inference, mapping update. No human. |
| Tier 2 — fix and notify | InferrexAI proposes a resolution and presents it for approval. |
| Tier 3 — escalate | For business-judgement calls. Surfaces a specific question. |
Inferrex notices the provider changed before your pipeline breaks.
Self-healing has to know when to fire, and Change Monitoring is how it knows. Make the invisible engine visible, and the self-healing stops looking like luck.
The normal failure mode for an API change is that you learn about it last. The provider ships a new field name or removes an endpoint, your pipeline keeps running on yesterday's assumptions, and the first signal you get is an error — downstream, after the break, when it's already a problem. Change Monitoring gets ahead of that sequence. It reads the change at the source, recognises it as something that actually matters, and lets the platform adapt before the break ever propagates to you.
Continuous watching
Inferrex watches the signals every provider broadcasts about itself: documentation, RSS feeds, public GitHub repositories, changelogs, and machine-readable OpenAPI specifications. Continuously — not on a schedule that checks once a week and misses everything in between. Different providers signal in different places, and Change Monitoring reads all of them.
Structural fingerprinting
Providers touch their documentation constantly; the overwhelming majority of those edits don't matter. A reworded sentence is not a breaking change. A removed field is. Change Monitoring fingerprints the *structure* of what it watches, so what reaches you is the change that affects you — not an alert every time a provider fixes a typo. That's the difference between a monitor you trust and one you mute.
Know what you're wasting and what you're missing.
Data value classification
Every entity rated Active, Reference, Archival, or Waste — waste surfaced as a monetary estimate.
Gap detection
Identifies data flows that should exist but don't, with coverage scores.
Entity resolution
One customer, one record, every system — duplicates merged, field authority applied.
Pipeline suggestions
When you approve a mapping, Inferrex proposes the next one — the pipeline that follows naturally from what you've just connected. You're not staring at a blank canvas wondering what to wire up next; you're approving a suggestion the platform already reasoned its way to.
The full second class of value — Stack Map, Data Model, Consolidation, Dark Data, Activation, Enrichment, Stack Bloat, Migration — has its own page: Stack Intelligence.
Operational resilience for your integrated data — not just cold storage for compliance.
The thread running through all of it is reversibility. When an integration misfires, a sync corrupts a batch, or a bulk operation does the wrong thing to the right records, the question that matters is whether you can undo it. With Backup & Recovery, you can.
"Archival" usually means a place to put old data so an auditor stops asking about it. Useful, but passive. Backup & Recovery is the active version: it snapshots before a destructive write, restores you to a known-good state after a bad one, serves archived records back so nothing downstream ever sees a gap, and produces a proof for every deletion. Five jobs, one purpose: make your integrated and golden-record data recoverable.
Pre-write snapshots
The backup, taken at the only moment that matters: immediately before a destructive write. The version you'd want to roll back to is the one from a second ago, not from last night's backup window.
Point-in-time restore
A bad write is not a permanent loss. Restore returns your integrated data to a known-good state from before the damage — scoped to the records affected, drawn from the snapshots taken before each change. Snapshots make sure the good version exists; restore puts it back.
Archival
Records move hot → warm → cold on a lifecycle policy you define. Recent, active data stays fast; older data moves down the tiers where it costs a fraction to hold. You set the rules once; the lifecycle enforces them from then on.
Transparent retrieval
Archived records are served back on demand, so the system asking never knows the difference between hot data and cold. You get the cost savings of tiering without things mysteriously not being there.
Deletion certificates
A log line isn't proof; it's a claim. Every deletion through Inferrex produces a compliance certificate — a record of what was removed and when, structured as evidence you can hand to an auditor or attach to an erasure response.
Compliance, inherited
The same per-jurisdiction rules engine that governs the rest of the platform drives retention windows, right-to-erasure, and legal hold here too. The engine knows what the data is and enforces the rule that applies to it.
See how it works across the platform.Scope, stated plainly for accuracy.
Your data already holds the signals.
Inferrex surfaces and structures them so you can build loyalty, rewards or engagement on top — you decide how it's delivered, we make the data make sense.
You can only gamify what you can understand. Points, streaks, levels, rewards — every engagement mechanic is built on signals: the actions a user takes that are worth recognising. Those signals already exist in your data. The problem is that in most stacks they're scattered across systems in inconsistent shapes, and no single tool can read them coherently enough to act on. The data is there. It just isn't legible.
Inferrex is not selling gamification as a product. It's applying the comprehension thesis to one more problem: it surfaces the engagement signals already present in your data, structures them into something you can build on, and hands you the logic to configure. What you do with that — the experience your users actually see — is yours.
Where this came from.
Canonical engagement events
The meaningful actions already buried in your data, expressed as structured events organised by role and facet. The same action recognised the same way regardless of which system it originated in.
Configurable accrual logic
You define the rules: how points accrue, what constitutes a streak, where level boundaries sit, which events count for what. Inferrex's job is to make the underlying events legible and reliable enough that your rules have something solid to run against.
An immutable, idempotent ledger
Every engagement event is recorded once and stays put. Tamper-evident, so history can't be quietly rewritten; idempotent, so the same event can't be double-counted on a replay. The points you award rest on a record you can trust — which matters the moment real rewards are attached.
What you own: the front-end. The badges, the leaderboards, the progress bars, the reward flows — the entire experience your users see and touch is yours to design and deliver. This division is deliberate, and it's the point: a fixed experience is exactly the constraint you don't want. The value is your own data made legible enough that you can build precisely the engagement experience you intended, with the hard part — making the signals coherent — already done.
Works from your AI IDE. No portal required.
The REST API is live, with API keys you create in-app (inf_live_…). The developer tooling sits on top of it, and any agent or IDE that speaks Model Context Protocol uses Inferrex as a tool.
- REST API — every platform capability over HTTP, authenticated with in-app API keys. Live today.
- InferrexSDK —
@inferrex/sdk, a type-safe wrapper over the full REST API. - InferrexMCP — create connections, approve schemas, and build pipelines in natural language. At launch.
- InferrexCLI —
npx @inferrex/mcp— no Docker, no Kubernetes. At launch. - Listed in the major MCP directories — Smithery, mcp.so, Claude, Cursor, and Windsurf.