The short version
fluex is a multi-document, ReAct-architecture document AI platform with pay-per-page pricing, multi-LLM extraction, and a complete platform stack including review queues and immutable audit trail. Hyperscience is a mature IDP platform with strong enterprise penetration in government, banking, and insurance, deep human-in-the-loop tooling, and on-prem deployment as a first-class option.
Capability comparison
| Capability | fluex | Hyperscience |
|---|---|---|
| Primary positioning | Multi-document, agentic ReAct architecture for AI-native teams | Enterprise IDP platform for highly-regulated industries (government, banking, insurance) |
| Pricing model | Pay-per-page, no setup fee, monthly minimum on usage tiers | Enterprise contracts, typically high six-figure to seven-figure annual commits |
| Document type coverage | 40+ generic document types out of the box, custom on Enterprise | Custom-trained per document type via supervised classification + extraction blocks |
| Custom model training | Few-shot config + active-learning queue, no separate training step | Dedicated training pipeline with annotation tooling and supervised fine-tuning |
| LLM strategy | Multi-provider (OpenAI + Anthropic) with zero-retention configured | Proprietary deep-learning models; LLM integrations available but not LLM-first |
| Time to first extraction | Minutes — 6 doc types live on Starter | Weeks-to-months — annotation, training, and validation cycle for each new doc type |
| Human-in-the-loop tooling | Review queue with confidence-based routing, audit-tracked corrections feeding active learning | Best-in-class HITL workflows with deep keyer ergonomics and annotation analytics |
| Compliance posture | SOC 2 Type II in progress, GDPR DPA, CCPA service-provider, HIPAA BAA on Enterprise | SOC 2 Type II, ISO 27001, FedRAMP Moderate (HSGov), HIPAA-ready deployments |
| Hosting | GCP (USA), single-tenant VPC on Enterprise, optional CMEK | SaaS, private-cloud, and full on-prem (air-gapped) deployments |
| Audit trail | Immutable per-request log with model version, prompt hash, response, reviewer trail | Comprehensive activity log with strong human-action lineage |
| API ergonomics | REST + webhooks, OpenAPI 3.1 spec, sync & async modes | REST API, mature SDK ecosystem, complex but well-documented |
| Best fit | AI-native teams shipping docs into product workflows, mid-market through enterprise | Large regulated enterprises with established ops teams and FedRAMP/air-gap requirements |
When to choose which
Choose Hyperscience when…
- You're a federal agency or regulated bank with FedRAMP, air-gapped, or fully on-prem deployment as a hard requirement.
- You have a dedicated keyer/ops team processing high-volume forms and need the deepest HITL ergonomics on the market.
- You're standardizing on supervised models for regulatory defensibility and prefer not to depend on third-party LLM APIs in the critical path.
- You need ISO 27001 and FedRAMP today, not in 12 months — Hyperscience has them and we don't yet.
- Your budget supports a six- or seven-figure annual commit and a multi-month implementation.
Choose fluex when…
- You process more than forms — payslips, statements, contracts, IDs, KYC docs in the same workflow without separate training pipelines.
- You want to start today with pay-per-page pricing and no annual commit.
- You need agentic / ReAct workflows — multi-step reasoning where extraction is one step in a longer chain.
- You're embedding document AI in your own product rather than standing up a back-office ops team to operate it.
- You need an audit trail with full LLM lineage — model version, prompt hash, response — per request, not just per human action.
- You want multi-LLM consensus as a built-in accuracy lever rather than a single proprietary model.
Architecture difference: supervised classification vs. agentic reasoning
The fundamental architectural difference matters for buyers. Hyperscience trains a supervised classification + extraction pipeline per document type. The pipeline is fast, predictable, and defensible — you can show an auditor exactly which model version produced each field and what training data it saw. The cost is the cycle time: each new document type requires annotation, training, validation, and deployment.
fluex's ReAct architecture treats extraction as a multi-step reasoning task: classify, plan, extract, validate, escalate. New document types come online by writing a schema, not by training a model. That's faster to iterate, and it generalizes to documents you've never seen before. The cost is more nuanced auditability — we solve this with full LLM lineage on every request (model version, prompt hash, response) so the audit trail captures the reasoning, not just the model artifact.
Neither architecture is universally better. If your document set is fixed and your accuracy bar is "regulator-defensible to within a basis point," Hyperscience's supervised approach is hard to beat. If your document set is growing and your team is small, fluex's general-purpose approach gets you to production faster.
Total cost of ownership
Hyperscience contracts are typically structured as multi-year commits with substantial implementation services — annotation work, custom training pipelines, and integration. Total first-year TCO for a mid-market deployment usually lands in the high six to low seven figures, with most cost in the first 12 months. fluex's pay-per-page pricing means a 100k-document/year deployment lands in the low five figures, with no implementation services required for the standard document types.
The TCO crossover happens around 8–10M documents/year of supervised forms — below that, fluex is significantly cheaper; above it (and especially with FedRAMP requirements), the curves narrow.
Deployment options
Hyperscience offers fully air-gapped on-prem deployment, which fluex does not. For federal agencies and some defense-adjacent banks, this is non-negotiable and ends the conversation. fluex offers single-tenant VPC deployment on Enterprise (your own GCP project, isolated network, optional CMEK), which covers most commercial compliance concerns without an on-prem footprint. If your security policy explicitly requires air-gap, Hyperscience is the answer.
Switching considerations
If you're evaluating fluex against Hyperscience as your incumbent, the practical pieces matter:
- Schema portability — fluex emits clean JSON; if your pipeline already consumes Hyperscience extraction blocks, mapping is typically a 1- to 2-day translation layer.
- Side-by-side evaluation — we run a 7-day evaluation against your real documents alongside your existing Hyperscience workflow. You get an accuracy and latency report you can show your CTO.
- No annual commit — start with a month-to-month plan. The pay-per-page pricing means you only pay for what you actually process.
- Coexistence — many teams run both for a transition period, with Hyperscience handling regulator-defensible forms and fluex handling everything else (contracts, IDs, statements, ad-hoc docs). Coexistence is usually easier to sell internally than a hard switch.
For the full security and compliance posture, see our trust page. For pricing, see pricing. For a side-by-side evaluation against your current workflow, talk to our team.