Every company will run on agents. The ones that win will have built their operating system intentionally — workflow by workflow, on infrastructure designed to scale. Inneall is that infrastructure.
Inneall is the platform on which Company OS deployments are built. It is not a consulting methodology — it is licensable software infrastructure, developed and validated across 13 enterprise engagements. Every workflow a client automates runs on the same engine.
The core execution layer for closed-loop agent workflows. The agent does the work end-to-end — document extraction, classification, generation, routing. Humans review exceptions only. Not a Copilot. A replacement for the manual process entirely.
Sequences multi-step workflows across systems — ingestion, processing, routing, posting, notification. Handles conditional logic, retries, and state management. The connective tissue between your data sources and your systems of record.
Continuous accuracy measurement against pre-agreed baselines. Confidence-based routing: high-confidence outputs pass through automatically; uncertain outputs route to human review. No silent failures. No data gaps between states.
Full audit trail on every agent decision. Human-in-the-loop specifications built into every workflow at the right decision points. Compliance-ready architecture — HIPAA, FFIEC, and SOC 2 compatible by design.
Connects to client systems of record — CRMs, ERPs, document management, healthcare platforms, financial systems. Works alongside existing tools. No system replacement required. Additive, not disruptive.
Persistent context across workflow steps and sessions. The engine retains client-specific knowledge, document schemas, and processing history — so each deployment gets smarter with every document it processes.
Every workflow the engine runs generates metrics. The monitoring layer surfaces them at three levels of resolution — from individual document accuracy to enterprise-wide ROI — so every stakeholder sees exactly what they need to see.
| Workflow | Status | Accuracy | Zero-touch | Docs / day | Value / yr |
|---|---|---|---|---|---|
| Insurance intake extraction | Live | 88% | 1,847 | $198K | |
| Claims triage & routing | Live | 81% | 634 | $142K | |
| Compliance document audit | Live | 79% | 290 | $97K | |
| Renewal notification engine | Calibrating | 52% | 180 | $50K proj. |
Most AI systems are static — they perform at deployment and drift from there. Inneall is designed to improve continuously. Every human-in-the-loop correction enriches the knowledge base, increasing confidence and reducing the volume of documents that need expert review. HITL is not a cost — it is the training signal.
Engagement begins with golden and adversarial dataset population (kickoff phase). HITL volume is highest early — each expert correction increases the engine's confidence on that document pattern. By steady state, 90%+ of documents pass through without human review.
Low-confidence documents shrink fastest as the golden dataset grows. The residual Low tail — novel patterns the engine hasn't seen — drives the adversarial dataset.
Every engagement begins with structured data collection: historical documents, known-correct outputs, domain terminology. The larger the golden dataset at kickoff, the higher the starting confidence — and the faster the engine climbs the learning curve.
Low and medium-confidence outputs route to the domain expert. Each expert response — whether confirming or correcting the agent — feeds directly into the confidence model. Disagreements become new golden examples; the engine improves on its next eval cycle.
The most dangerous mistakes are the ones the engine makes with high confidence. It maintains a library of cases where it was confidently wrong — document patterns, edge cases, ambiguous inputs — so the engine never makes the same mistake twice at scale.
A document-heavy workflow — the most common starting point. The engine doesn't replace one step: it automates the entire chain from ingestion through downstream action, with human judgment applied only where it genuinely matters.
Copilots help people type faster. Inneall replaces the manual process entirely. Humans review exceptions — the engine does the work end to end.
Ingestion, extraction, routing, posting, notification — one connected workflow. The engine orchestrates every step, not just the one that looked easy to automate.
The infrastructure from Layer 1 makes Layer 2 faster and cheaper. The Company OS is an asset that grows in value with every workflow added.
Thirteen engagements delivered. Four representative examples — anonymized at client request. Detailed case studies available to Solutions Partners via the partner portal.
A healthcare regulatory analytics platform processing 10,000+ compliance documents annually at $15/document — $150K/year in manual labor costs that scaled linearly with every new state entered. Growth was gated by headcount.
Inneall built a confidence-based extraction pipeline across 46 state document formats. High-confidence extractions processed automatically. Uncertain documents routed to human review with full audit trail. Medical-grade accuracy required given documents underpin $30M+ investment decisions.
A healthcare and legal marketing SaaS with 1,800+ clients. A 5-person onboarding team spending 64% of their time on manual admin: chasing clients for missing inputs, copy-pasting AI meeting summaries into the CRM, relaying developer feedback by hand.
Inneall deployed an email intelligence agent that monitors enterprise email streams, extracts structured data, and posts to the CRM in real time — with human-in-the-loop decision support via Google Chat. Additive to existing systems; no replacement required.
A commercial real estate mortgage banking network managing 1,600+ loans across 47 states. Core operations running on Excel and Word. 30–90 page loan submission packages assembled manually over days. Insurance compliance monitored by hand across the full portfolio.
Inneall identified five high-priority automation opportunities and built interactive ROI calculators from the client's own data. The solution architecture was designed for replication across a 4-office national network — configuration, not reinvention, at each location.
A healthcare digital marketing platform serving 1,000+ medical and aesthetic practices. Acquired through a search fund with board pressure to shift from a headcount-dependent agency model to a scalable technology platform. Net Revenue Retention under pressure; no mechanism to grow client value over time.
Inneall identified the structural advantage: performance data across 1,000 same-vertical clients, synthesised, creates intelligence no individual client can replicate. Scoped blog production and SEO workflow automation as the wedge; cross-client intelligence engine as the long-term competitive edge.
Most AI tools charge by the seat or by the API call — regardless of whether the work actually got done. We charge for value delivered. Your cost ties directly to the labor your team no longer does.
We define your workflow, build the engine, capture baselines, and put it in production. You know exactly what you're paying upfront — no scope-creep surprises, no invoice-day shocks.
Once live, you pay per workflow article the engine handles end-to-end. Articles that still need human review cost less — because you didn't get the full automation. Articles the engine can't process cost zero.
As the engine improves, auto-pass rates rise. Your per-article cost stays — your overall labor cost drops faster than your bill rises. Net savings grow as we deliver.
Your maximum spend caps at your current manual cost. Outcome pricing isn't variable in a scary way — the most you'd spend is what you're already spending on the labor we displace. Everything below that is savings.
Need predictable monthly budgeting? Procurement-heavy environments, government, and certain regulated industries can choose our subscription tier with annual true-up. The default is outcome-aligned; subscription is the option.
Inneall.ai workflows in production are delivered by Inneall.ai directly or by certified Solutions Partners. Whichever way it reaches you, the engine, eval framework, governance layer, and audit trail are identical — and every deployment displays this badge. Your assurance: same quality bar, regardless of who delivered it.
Are you an agency or consultancy? Learn about the Solutions Partner Program →
Eight weeks from signed agreement to your first workflow in production — you own everything we build. Start with a 30-minute discovery call: we identify one candidate workflow, score it against four criteria, and tell you whether it's worth building. No pitch. No proposal pressure. If the workflow scores below threshold, we'll tell you that too.