AI Product Infrastructure

Ship AI features your
customers can trust.

Your AI products are only as trustworthy as the classification layer they run on. Hypericum builds and governs that layer.

Symptoms of an ungoverned classification layer

The AI feature works in your proof of concept. It fails in production. The AI model cannot fix what the classification layer got wrong.

01
Hallucination by inconsistency
The model encounters the same concept under different labels and treats them as different things, or conflates distinct concepts that happen to share a label. The output is confident and wrong.
02
Unreliable retrieval
RAG systems retrieve documents that match the query's surface form but not its intent, because the underlying taxonomy was never governed. Semantic search returns plausible results, not accurate ones.
03
Client data mismatch
Results that are accurate for one client's data model are wrong for another's. The feature cannot ship as a standardized product capability. Every client becomes a bespoke configuration problem.

See it in action

Talentis is a fictional HR SaaS platform with 800 enterprise clients. Each panel shows Talentis' AI assistant responding to the same employee query: one with a semantic classification engine and one without.

Without semantic classification engine Fragmented client data, no shared taxonomy
?

Select a question above to see both responses

With semantic classification engine Canonical taxonomy — unified classification applied
◎

Governed response will appear here

Responses are illustrative. Talentis is a fictional platform created for demonstration purposes.

What Hypericum fixes

Three specific problems that block AI features from shipping reliably across a multi-tenant software platform.

01
Internal data schema fragmentation
Software products accumulate semantic debt across versions, product lines, markets, and acquisitions. The same concept (a customer record, a cost code, a job category) is often defined differently in different modules or regional deployments. AI features that traverse the full product surface fail at the joins. Hypericum builds the governed canonical layer that resolves these internal inconsistencies before they propagate into AI outputs.
02
Fragmented client classifications
In platforms where clients bring or configure their own taxonomies, the AI layer operates across a surface where the same concept is described in dozens of different ways across the client base. The AI cannot generalise across clients and cannot ship standardised intelligent features. Hypericum builds the alignment layer that maps client-specific classifications to governed canonical definitions, without forcing clients to change their own terminology.
03
Analytics that cannot generalise
Customers are increasingly demanding AI-enabled analytics: inconsistency at the semantic layer limits apples-to-apples comparability and reduces the level of insight you can give them. For internal analytics, cross-customer benchmarking, portfolio-level intelligence, and marketing granularity is also impacted, reducing the ability to target marketing and make volume-price decisions.

What we build

The classification layer your AI operates against. Governed, versioned, multi-tenant where required, and built to hold across every client implementation.

01
Platform taxonomy audit and design
Map the classification gaps across your client data model. Identify where the same concept is described differently across implementations. Design a governed canonical taxonomy that resolves the inconsistency without requiring clients to re-onboard.
02
Semantic control layer deployment
Build the classification layer as a governed, versioned asset: formal taxonomic structures your AI feature can query against reliably. Not a static schema. A runtime layer that enforces consistent meaning across every client's data at inference time.
03
Multi-tenant classification governance
For platforms where clients bring their own data models, Hypericum builds the mapping layer that translates client-specific classifications into a governed canonical structure. Your AI feature operates against the canonical layer, not against each client's raw implementation.
04
AI feature unblocking
Scoped engagements designed to unblock a specific stalled feature: semantic search, copilot outputs, recommendation engines, agentic workflows. Hypericum identifies the classification gaps blocking the feature, builds the governed layer that resolves them, and delivers against your product sprint timeline.

How it works

Productized delivery designed to fit inside a product sprint budget. Every engagement begins with a Blueprint.

01
Blueprint
2 weeks · Fixed price
  • Map classification inconsistencies across your client data model
  • Identify the specific gaps blocking your AI feature in production
  • Deliver a designed specification for the semantic control layer
  • Phased implementation roadmap scoped to your product timeline
02
Classification layer build
4 to 12 weeks
  • Governed canonical taxonomy built and versioned
  • Client-to-canonical mapping rules created and validated
  • Runtime classification layer exposed to your AI feature
  • Tested against real client data before production deployment
03
Governance and expansion
Ongoing
  • Version control as your taxonomy evolves
  • New client onboarding mapped to governed canonical structure
  • Classification layer extended as additional AI features ship

Start here

Every engagement begins with a Semantic Control Layer Blueprint. Two weeks. A precise diagnosis of what is blocking your AI feature and a designed specification to fix it.

Semantic Control Layer Blueprint
Fixed price · Delivered in two weeks · Scoped in advance
  • Classification inconsistencies mapped across your client data model
  • Specific gaps blocking your AI feature identified and documented
  • Governed canonical taxonomy designed for your platform
  • Client-to-canonical mapping approach specified
  • Phased build roadmap with cost and timeline estimates

Deliverables agreed in writing before work begins. Funded from product sprint budget, not IT governance.

Talk to us

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