About Inferon Labs
AI automation, engineered for production.
Inferon Labs exists for companies that need automation to do more than look impressive in a demo. We build production-grade AI automation systems for B2B operations, sales, support, and back-office teams.
Mission
Build automation systems that serious teams can trust in production.
That means automation with clear ownership, measurable business value, human oversight where needed, and the observability required to operate safely at scale.
Why we exist
Most companies are not struggling because they lack tools.
They already have a CRM, a support desk, a project management system, spreadsheets, Slack, email, forms, databases, and AI subscriptions.
The problem is the workflow layer between them. People still move data manually. Teams still route work by memory. Reports still require cleanup. Support still depends on repetitive triage.
Inferon Labs exists to build the missing operating layer.
Inferon Labs
Boutique automation engineering for high-stakes B2B workflows
We work with a limited number of clients on workflows where failure is costly — manual operations at scale, cross-system handoffs, and AI decisions that require governance, traceability, and production discipline.
- Deep workflow mapping before any build commitment
- Production architecture across orchestration, APIs, and data layers
- Human-in-the-loop AI with defined escalation and approval
- Staging environments, monitoring, and operational runbooks
- Delivery structured for handover — not dependency on the builder
Who we are
A senior-led engineering firm — not a generalist agency.
Inferon Labs is a boutique automation engineering practice. We work with a limited number of B2B clients at a time and focus on workflows where reliability, approvals, and production behavior matter.
Our work spans workflow orchestration, API integration, CRM and support automation, AI-assisted decision layers, databases, containerized deployments, and operational handover — built for teams that need systems they can run after delivery.
We do not trade on inflated headcount or vague transformation language. We trade on architecture, precision, and accountable delivery.
Engineering philosophy
How we work
Structured. Scoped. Outcome-focused.
Inferon Labs works with a small number of clients at a time. The goal is not to add as many automations as possible. The goal is to build the workflows that produce the highest operational value with the least unnecessary complexity.
What we believe
About AI automation and production systems.
AI automation should not remove judgment from a business.
It should remove repetitive work around judgment. The best systems define where AI can help, where it must be validated, and where a human remains accountable.
Automation that breaks silently is worse than no automation.
An automation that works 95% of the time and fails 5% of the time without detection is not a working system. It is a hidden operational risk.
The deliverable is not the workflow. It is operational clarity.
A production-ready system includes documentation, monitoring, approval steps, and ownership — so the team can run it without depending on the builder.
Tools & stack
Built with production-grade tools — not hype stacks.
Inferon Labs works with the orchestration, data, and integration tools your team already uses.
Orchestration
- n8n
- Make
- Custom API services
AI Models
- OpenAI
- Anthropic
- Gemini
- Structured outputs
Data & Storage
- PostgreSQL
- Supabase
- Redis
- Airtable
Infrastructure
- Docker
- Env-based secrets
- Staging / Production separation
- CI/CD
CRM & Sales
- HubSpot
- Salesforce
- Apollo
- Clay
- Outreach
- Slack
Support & CS
- Zendesk
- Intercom
- Freshdesk
- Jira
- Confluence
Automation & APIs
- REST APIs
- Webhooks
- Playwright
- Browser automation
Monitoring
- Execution logs
- Workflow alerts
- Dashboards
- Runbooks
Framework
The Inferon Operating Layer
A six-part framework for building AI automation systems that survive production.
Most failed automation projects skip the infrastructure that makes automation safe to run in production. This framework ensures the system is mapped, integrated, governed, observed, and maintained before it becomes business-critical.
How we deliver
- Map the workflow and define approval boundaries before writing automation logic.
- Build and validate in staging — production deploys only after edge-case testing.
- Hand over with monitoring, runbooks, and documentation your team can operate.
Process Mapping
Map the real workflow, not the polished version. This includes triggers, handoffs, decisions, exceptions, workarounds, approval points, delays, and failure modes.
→ What actually happens today, step by step, when the workflow runs?
Data & Integration Layer
Identify the systems of record, data ownership, API access, credentials, field quality, and integration constraints.
→ Which systems need to communicate, and which data can be trusted?
AI Decision Layer
Apply AI only where it adds operational leverage. Good use cases include classification, enrichment, summarisation, drafting, routing, extraction, validation, and recommendation.
→ What decision or cognitive task should AI assist — and what output format is safe to use downstream?
Human Approval Layer
Define where humans stay in control. Human approval is essential for customer-facing outputs, high-value records, sensitive data, uncertain classifications, or irreversible actions.
→ Which steps should require human review before the workflow continues?
Logging & Monitoring
Make the system observable. Every serious workflow should capture execution state, inputs, outputs, errors, approvals, retries, and alerts.
→ If this breaks at 2 AM, how will the team know what happened and what to do next?
Optimisation & Maintenance
Improve based on production behaviour. APIs change, business rules evolve, prompts drift, and new edge cases appear. Maintenance turns automation from a project into infrastructure.
→ What needs to be reviewed monthly so the workflow stays reliable?
Credibility
An engineering firm built for production — not demos.
Inferon Labs applies senior automation engineering to B2B workflows where reliability, edge cases, and operational behavior matter.
What we deliver
- Workflow orchestration and API integration
- CRM, support, and back-office automation
- AI agent design with human approval gates
- Docker-based deployments with PostgreSQL and Redis
- Production delivery: staging, testing, monitoring, handover
What separates production-grade work
Data & security
Credentials stored as environment variables. Staging before production. Least-necessary access. Documented data flows. Privacy policy →
If your operations depend on manual handoffs, there is likely a better system.
The first step is an audit fit review. If there is a strong fit, the next step is a paid Automation Opportunity Audit.
Best fit for B2B teams with repeated workflows across CRM, support, operations, reporting, approvals, or back-office systems.