active research · not yet released

Correctness for
sophisticated languages.

A formal verification pipeline for structured document generation. Built for languages where precision is non-negotiable.

// patent prosecution  ·  smart contract audit  ·  compliance documents

Engineering first.
Domain second.
Every other approach starts with the domain and asks AI to fill the gaps. Synkti starts with formal correctness and instantiates it per domain. The pipeline enforces structure. The domain supplies the grammar.
Approach

A formal protocol for structured generation

Synkti establishes a correctness protocol, not a prompt wrapper. Structural and semantic invariants are enforced at every stage before generation proceeds.

Formal grammar layer

Domain-specific production rules. Hard invariants. Corpus-calibrated soft constraints.

Semantic consistency engine

Every term, reference, and relationship tracked across the full document graph. Violations surface at source.

Incremental validation

Each primitive verified before the next is constructed. Invalid state cannot advance.

Constrained generation

AI fills grammar-defined slots. Not free generation. Constrained from the start.

Traceable verdicts

Correctness claims trace to a derivation, not a confidence score. Every flag has a rule chain. Every rule chain has a citation.

Explicit abstention

The pipeline abstains rather than guesses. Uncertainty is stated, not hidden behind a probability.

Judgment maximization

96% handled by the pipeline. The 4% reserved for strategy, ambiguity, and scope belongs to the expert.

Two invariants.
Privacy. Correctness.
  • — No document reaches a server. Ever.
  • — Correctness is structural, not probabilistic.
  • — The pipeline cannot produce output it cannot verify.
  • — Expert judgment is protected, not replaced.
Domains

Same engine.
Different formal languages.

The core pipeline is domain-agnostic. Grammar, corpus, and semantic data layer swap per domain. First instantiations in active development.

Patent Prosecution

Claim language verification

  • — Antecedent basis checking
  • — Scope monotonicity enforcement
  • — Reference numeral consistency
  • — 112(b) surface detection
  • — Claim dependency validation
Smart Contract Audit

Specification verification

  • — NatSpec completeness
  • — Invariant coverage
  • — Access control documentation
  • — Audit report structure
  • — Interface specification consistency
Compliance Documents

Regulatory structure verification

  • — Required section completeness
  • — Cross-reference consistency
  • — Defined term tracking
  • — Obligation structure validation
  • — Evidence completeness
Architecture

Hybrid deterministic-neural architecture

  • — Deterministic rules enforce invariants
  • — Small local model fills constrained slots
  • — Neither operates outside its boundary
  • — Runs entirely client-side
  • — Rust / WASM / WebGPU
96%
research target

The 4% is not a gap.
It is the design.

The pipeline targets 96% standalone correctness across structural and semantic checks. The remaining 4% covers strategy, scope, and ambiguity, which is explicitly preserved for human judgment. A system that knows its boundary is more trustworthy than one that does not.

Privacy

Confidentiality
is the architecture.

The pipeline runs in the browser. No document reaches a server. A verifiable technical property, not a policy.

Zero data egress

  • — No document leaves the client
  • — Verifiable via network inspection

On-device inference

  • — AI runs locally via GPU
  • — No third-party in the data path

Privilege by construction

  • — Enforced architecturally
  • — Not a contract with a vendor
Stack
Rust · WebAssembly · WebGPU · JavaScript · Formal Grammar · Local Inference · Semantic Embeddings

Research in progress.

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