Taiso Reliability Infrastructure

Frontier AI you can prove.

A reliability layer for your AI applications. Every output verified, every tool call mediated, every block auditable.

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The real problem

Your skills file says
what should happen.
Nothing makes it happen.

Hallucinations reach production because nothing audits them.
Tool calls slip past your rules because nothing mediates them.
The model grades its own work because nothing else can.
Drift is invisible because there's no trail to compare.
Compliance reviews depend on hope because the evidence is prose.

Frontier vendors aren't selling you intelligence.
They're selling you self-grading.

Introducing Taiso

One endpoint.
Every output verified.

Taiso is the reliability layer that wraps your model calls in independent verification — audited, schema-correct, source-grounded, every time. Behind a single OpenAI-compatible URL, so the migration is a one-line config change.

You never have to defend an unverified output again.

How it works

Four primitives.
One reliability story.

Taiso isn't a framework — it's an opinionated substrate. Four primitives compose, each addressing one concrete failure mode of today's agent stacks.

01 · Verify
SLAF audit. Re-derives every verdict.
A verifier model performs two distinct searches per field — one for supporting evidence, one for contradicting evidence. Then deterministic code re-derives the classification from the array lengths alone. The LLM's self-reported answer loses on disagreement. 
# audit_service.classify_field()
if not support and not dispute: return "not_found"
if  support and not dispute: return "supported"
if  dispute and not support: return "disputed"
return "unclear"           # both? human review.
02 · Mediate
Intent loop. Plans and execution, separated.
The extractor reads what the agent wants to do. It has no tool access by construction. Two independent evaluators — security and alignment — gate the action against your constraint store. Indirect injection has to fool all three. 
# intent_service.evaluate()
intents = extractor.extract(turn)
for i in intents:
    sec   = security.check(i)
    align = alignment.check(i, north_star)
    if combine(sec, align) == EXECUTE:
        executor.run(i)      # only path to a tool
03 · Orchestrate
SOP engine. Workflows as stored procedures.
Declarative YAML compiled to executable DAGs. Every block can carry audit: true — the audit runs between blocks and gates downstream execution on the risk score. State checkpointed after every node. 
# your SOP — patent generator
blocks:
  - id: claims
    type: llm
    audit: true         # gate downstream
  - id: file
    depends_on: [claims]
    when: "claims.audit.risk != 'high'"
04 · Travel
Audit trail. The verdict carries the receipts.
Every intent extracted, verdict assigned, citation pulled, and tool call gated is written to both SQLite and an append-only JSONL log. Replayable. Queryable. Char-level citation positions. No reconstruction from prose. 
# audit envelope, per event
{"ts": "2026-05-09T10:42:23Z",
 "block": "claims",
 "field": "priority_date",
 "verdict": "disputed",
 "cite": {"src": "fil-87a",
          "start": 4112, "end": 4180}}
Without vs with — same request

A single request.
Two different futures.

Same input. Same model. Same source documents. Below: what a typical structured-output call returns today versus what Taiso returns. The difference isn't "more JSON." It's whether you can prove the answer to a regulator, a customer, or a build that ships at 9am tomorrow.

A · Vanilla LLM extraction Unverifiable
POST /v1/chat/completions openai-compatible 
// response.message.content
{
  "invention_title": "Reliable Agent System",
  "inventors": ["M. Chatterjee"],
  "priority_date": "2024-10-14",
  "claims_count": 12
}
No provenance Self-reported only Drift invisible
B · Taiso structured-blocks audit Replayable
POST /api/v2/structured-blocks/audit SLAF 
{
  "invention_title": {
    "value": "Reliable Agent System",
    "classification": "supported",
    "confidence": 0.94,
    "citations": [{
      "source_id": "fil-87a",
      "char_start": 412, "char_end": 449 }]
  },
  "priority_date": {
    "value": "2024-10-14",
    "classification": "disputed",
    "confidence": 0.18,
    "disputing_evidence": [{
      "quote": "filed 2024-11-08",
      "char_start": 4112, "char_end": 4180 }],
    "discrepancy": "extracted Oct, source says Nov"
  },
  "_summary": { "risk": "medium", "problem_ratio": 0.25 }
}
Per-field provenance Re-derived verdict Replayable
Architecture

Built like infrastructure,
not like a wrapper.

Three loops, one shared substrate. The extractor cannot run tools. The evaluators cannot run tools. Only the executor can — and only on intents that passed both axes. Every event is durable. Every event is replayable.

User turn / goal Agent response / tool Intent extractor NO TOOL ACCESS structured intents action · target · context Alignment evaluator project north star absorbed user rules Security evaluator destructive · injection policy rules · regex Verdict EXECUTE · ESCALATE DEFER · ABSORB · BLOCK Executor runs the tool only on permit Audit trail SQLite + JSONL
The extractor cannot run tools. The evaluators cannot run tools. Only the executor can — and only on intents that passed both axes. Every event lands in SQLite and an append-only JSONL log.
What you get

Two guarantees.
One layer.

R
Reliability, by construction.
Every output independently verified, every tool call mediated, every event in a durable audit trail — re-derived, not self-reported.
F
Freedom from model choice.
When a faster, smarter, cheaper model ships next week, your code doesn't change. Taiso routes to it. Your application never knows.
Built on Taiso

Taiso Voice.
The platform, in production.

The first domain agent built on Taiso — same verification, same intent mediation, same audit trail, tuned to a single job.

Taiso Voice
24/7 AI phone agent for trades businesses.
Picks up every call. Sounds human. Reads live data from your shop. Books work directly. Recovers the revenue you're losing to missed calls.
Visit Taiso Voice →

More domain agents on the same substrate — get on the list →

Talk to us

Stop hoping.
Start proving.

If your agent is going to write code, file documents, move money, or touch patient data — the question isn't whether it works in a demo. It's whether you can defend what it did on a Tuesday morning.

Contact us → Email the team