Magnitude Engine

Every regulated industry is independently rebuilding the same AI agent stack. Magnitude is that plumbing, productized.

Insurance claims teams, financial-research desks, pharma compliance groups, and back-office HR functions are all solving identical infrastructure problems in their own silos — each hiring expensive AI engineers to reinvent the plumbing before they ever ship a feature. A business defines its workflow visually or in code, points it at its own data, writes a prompt per step, and ships. Durable execution, model choice, compliance certifications, and full trajectory-level observability come out of the box.

The problem

AI is no longer a research problem — it's an integration, reliability, and compliance problem. The hard part isn't calling a model; it's everything around the model:

• Integrations are bespoke and brittle. Every team writes its own connectors to its own corporate systems, then maintains them forever.
• Reliability is hard. Multi-step agent processes need durable execution, retries, and state management — most teams discover this the painful way, in production.
• Compliance is a blocker, not a feature. In insurance, finance, pharma and healthcare, a workflow can't ship without GDPR, ISO, data-residency and audit guarantees. Bolting these on after the fact is slow and expensive.
• Agents are black boxes. When an agent produces a wrong outcome, teams have no good way to trace why — which step, which prompt, which input led there.
• Talent is scarce and expensive. The reflexive answer is "hire AI engineers," but that means months of ramp and salary to rebuild infrastructure that isn't differentiating.

The result: a long tail of vertical AI startups and enterprise teams each spending their first two quarters rebuilding the same substrate before delivering any business value.

The solution

Magnitude Engine is a horizontal AI-infrastructure platform — an orchestration layer that sits between a business's data and the frontier models, and handles everything in between. A workflow is a graph. Each step has three parts:

• Input — pulled in through connectors the business defines (in code or via the SDK).
• A system prompt — injected into the model of choice (Claude, Llama, Gemini, OpenAI, etc.).
• An output — passed to the next step.

Chain those steps and you have a complete process — claim triage, a compliance check, a research brief — running reliably in the cloud. The same workflow can be authored three ways: visually in a snappy canvas/spreadsheet UI for business users, declaratively as YAML for complex DAG workflows, or programmatically via an SDK (Python, Go, and more) with first-class API keys.

Built on AWS Step Functions, every workflow gets durable execution and effectively infinite scale by default. The user picks the model and the AWS region — including models served through Amazon Bedrock — so the same workflow can run inside the compliance and data-residency boundary the industry requires.

What makes it different

Magnitude isn't competing on a single axis — it's the combination that's hard to replicate.

• vs. building it yourself / hiring AI engineers. Magnitude ships the infrastructure, integrations, compliance scaffolding, and observability on day one. Teams spend their engineering budget on their domain, not on plumbing.
• vs. vertical point solutions. Today's finance-, healthcare- and compliance-agent startups each build intelligence in their own silo. Magnitude is the substrate they could build on top of, not another point solution beside them.
• vs. durable-execution frameworks (e.g. Temporal). Durable execution is necessary but not sufficient. Magnitude adds compliance certifications, battle-tested domain learnings, deep corporate integrations, and LLM-native observability. It's durable and compliant and auditable and domain-aware.

Who it's for

Magnitude targets any industry with structured back-office and administrative work — starting where the pain and the compliance bar are highest:

• Insurance — claims intake, triage, and processing.
• Financial services — agentic research analysts and reporting.
• Pharma & healthcare — fast-tracking compliance and regulated admin workflows.
• Real estate — document- and process-heavy operations.
• HR & recruiting, and back-office generally — the long tail of repeatable administrative processes.

The pattern is consistent: high-volume, rules-heavy, compliance-sensitive processes that are perfect for reliable agents working tirelessly — and painful to automate without exactly the infrastructure Magnitude provides.

Competitive landscape

The market is already proving the thesis: well-funded teams are building exactly the kind of compliant, workflow-driven AI that Magnitude generalizes — but each one rebuilds the full stack inside a single vertical. That fragmentation is the opportunity.

Vertical point solutions — proof the demand is real

• AI for financial-advice firms. Well-funded players have built their own data capture, workflow automation, a compliance suite, and deep two-way back-office integrations — serving hundreds of firms and thousands of advisers. A single vertical justified an entire purpose-built stack.
• AI teammates for healthcare admin. Referral triage, waiting-list validation, booking and patient comms; architectures almost identical to Magnitude's per-step model, but hardwired to one domain. The workflow-graph-with-validation pattern works and sells.
• Tech-enabled claims services. Providers that run claims as a service rather than selling a platform. Many regulated buyers want the outcome done for them — a natural design partner or customer for Magnitude, not a rival.

The closest platform analogue — and the clearest cautions

• Single-modality agentic platforms. The most structurally similar players pair a no-code builder, a developer SDK, enterprise compliance by default (SOC 2, HIPAA, GDPR, PCI DSS), and a proprietary model — but live in one modality, such as conversational voice for contact centers. Lesson: the "builder + SDK + compliance-by-default" shape scales and raises money — and winners own a wedge completely and build proprietary IP. Model-routing alone is not, by itself, that IP.

Where Magnitude is genuinely differentiated: vs. the vertical players, it is horizontal and multi-vertical by design, model- and region-flexible, with an on-prem path — the substrate beneath the next wave of vertical agents. vs. the voice-platform analogues, a different modality entirely: they orchestrate real-time conversation; Magnitude orchestrates back-office processes — durable, multi-step DAGs over connectors and corporate systems.

Strategic questions this landscape forces

• Horizontal engine vs. vertical solution. Is the customer the vertical builder (an infrastructure play for regulated AI agents) or the end enterprise (which likely means going vertical first)? The cleanest story: win one beachhead vertical deeply, then generalize the engine underneath it.
• What's the durable moat? Model access is commoditizing. Defensibility lives in the bundle competitors treat as an afterthought — compliance certs, cross-domain learnings, integration depth, trajectory observability — plus proprietary IP (evals, domain templates, a fine-tuned routing/guardrail layer). Must be made concrete.
• Build-vs-buy at the vertical layer. Today's vertical players built their own infra. Why will the next wave choose Magnitude? Likely: speed-to-compliant-production and the on-prem story — but it must be evidenced.

Product roadmap

• Now — Cloud, model-flexible, compliant. Visual + YAML + SDK authoring, durable execution on AWS Step Functions, model and region selection across Bedrock, core compliance certs, and trajectory observability.
• Next — Platform & ecosystem. Public SDK in Python, Go and more with API keys, deeper corporate integrations, and the Slack/Workspace debugging bot.
• Later — Run anywhere, including on-prem. Host the engine on customers' own GPUs on-premise, orchestrated via Kubernetes — for the most tightly regulated environments.

Why now

Frontier models are finally good enough to run real administrative work, but the surrounding infrastructure — reliability, compliance, observability — hasn't been productized. Cloud primitives like Step Functions and managed model access via Bedrock make a horizontal, compliant orchestration layer buildable today in a way it wasn't 18 months ago. The market is fragmenting into vertical point solutions; whoever ships the trusted substrate underneath them captures the platform layer.

Business model

Magnitude is a usage-based platform: a per-workflow / per-execution consumption tier on top of model costs, with enterprise plans for on-prem deployment, advanced compliance, and dedicated support. Pricing scales with the work the engine runs — not with seats — so cost tracks directly to value delivered.