ForgeAI

The control layer that lets you wield AI with confidence

Govern the input, context, and output of your AI pipeline.

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SalesSemantic MarkerSemantic Control LayerGovernance & Orchestration

Structured inputs. Controlled context. Defensible outputs. Measurable outcomes.

Your CTO

Can't make sense of scattered, unstructured information

The answers are already in your systems, but trapped in PDFs, emails, and siloed data lakes.

Engineering ends up rebuilding the same extraction logic for every new document type, every new source, every new schema.

Unstructured dataSiloed sourcesBespoke pipelines

Your COO

Can't trust agents that operate without controlled context

Every decision depends on which policies apply, which data is relevant, which exceptions matter. Get any of it wrong and the agent acts on the wrong information.

Hard-coding context into pipelines doesn't scale. Letting the model guess is worse.

Agent reliabilityPolicy enforcementDecision context

Your CRO

Can't defend decisions without an audit trail

Critical decisions carry real consequences for customers, regulators, and the business.

Every output is grounded in a source, a concept, and the evidence chain that links them.

Source provenanceConcept traceabilityRegulatory defensibility

Your CFO

Can't greenlight another AI project without proven value

Every AI initiative starts with a business case. Few survive the gap between prototype and production.

The cost is not just the build. It is the domain experts, the maintenance, and the next project that starts from scratch.

Time to valueTotal costROI clarity

Governed queries that identify what is relevant, not what looks similar.

Compose four clause types into one precise query: concepts, entities, modalities, and time. Configure every query for recall, precision or a balanced approach. Every result is deterministic and auditable.

Optimization

Configure every query for your use case

  • "All" configures the query for recall: broad coverage that catches indirect or hedged language, willing to accept some false positives for monitoring use cases
  • Switch to precision to reduce noise: narrower thresholds, higher confidence, fewer false positives
  • The same Semantic Marker, configured for precision or recall, serves different teams and different questions without rebuilding anything
Example chunk
Following the Q2 incident, Supplier X is now contractually forced to maintain a 14-day inventory buffer to prevent recurrence of the production line shutdowns.

Embedding similarity scores this 0.18 against a "negative projections" query. The chunk is a forward-looking obligation arising from a past failure, but there is almost no lexical overlap. Standard RAG systems would never surface it. Recall-optimized Semantic Markers catch what similarity misses.

Semantic Markers deterministically identify concepts with measurable confidence. Each one produces quantitative metrics you can track and compare over time, across entities, or across concepts.

Trusted Intelligence

Govern input.
Govern output.

One primitive, deployed at every step.

Input Governance

Classify prompts before they reach your model. Block what shouldn't pass.

Context Control

Only relevant, on-policy context reaches your model.

Output Defense

Catch hallucinations and policy violations before they reach your users.

Full Traceability

Every output traces back to source, concept, and reasoning chain.

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