Applications And Market Map¶
Director-AI is a factual-coherence control plane for LLM applications. It sits between generated output and consequence: a user-visible answer, a streamed token path, a tool call, an agent handoff, a stored record, or an audit event.
Use this page when you need to explain the product quickly to a buyer, developer, evaluator, operator, or partner.
What The Software Is¶
Director-AI is not a chatbot, prompt template, content-moderation list, or model provider. It is a guardrail runtime that checks whether generated text is grounded enough to proceed.
The runtime combines:
- governed facts from inline rules, document ingestion, or vector retrieval;
- configurable scorers, including rules, embeddings, NLI, and structured verification;
- response-level factual-coherence scoring, with opt-in contradiction-driven streaming halt for completed streamed claims;
- SDK, framework, REST, gRPC, inference-server, and voice integration surfaces;
- tenant-safe evidence, audit, metrics, and forensics records.
The smallest useful path is director-ai-lite, which exposes a three-line
guard facade for free-tier adoption. The full director-ai package is the
open-core runtime, with Pro and Full adding production entitlement for the
BUSL capabilities. Director-Class AI is a separate action-control and evidence
product for high-impact agent operations; it is not a Director-AI wheel.
Who Needs It¶
| Audience | Primary problem | Director-AI value |
|---|---|---|
| Product owner | A wrong answer damages trust or triggers rework | Define which answers need fact gates and what evidence a pilot must collect |
| Application developer | LLM output reaches users before review | Add guard(), SDK middleware, or a REST proxy at the output boundary |
| RAG engineer | Retrieval context is stale, noisy, or incomplete | Score answers against governed facts and retain retrieval evidence |
| Runtime/platform team | Multiple apps need one shared control layer | Deploy REST/gRPC, auth, metrics, audit, and rollout controls once |
| Evaluation lead | Model, prompt, or KB changes need regression gates | Run batch scoring, thresholds, false-positive review, and benchmark cards |
| Governance/security team | Incidents need reviewable evidence without raw data exposure | Use tenant-safe events, compliance reports, and guardrail forensics |
Application Lanes¶
Pick one lane for the first pilot. Each lane has a different success signal.
| Lane | Protected workflow | First proof |
|---|---|---|
| Customer support | Refunds, warranty, policy, entitlement, and account answers | One known-good answer approved and one unsupported policy answer rejected |
| Enterprise knowledge assistant | Private-document answers and summaries | Verdict includes retrieved chunks and a traceable rejection reason |
| Regulated review | Medical, legal, finance, or research drafts | Unsupported claims route to human review with evidence and threshold rationale |
| Streaming assistant | User-visible token stream | Contradictory completed claim halts before the stream continues |
| Agent workflow | Tool output, chain step, or handoff | Unsafe step rejects or routes before downstream action |
| Evaluation pipeline | Prompt/response datasets and model updates | Batch report shows threshold, false positives, and false negatives |
| Platform deployment | Shared guardrail service | Auth, metrics, logs, rollback, and runbook evidence are visible |
Market Value¶
The market value is control over factual risk. Director-AI can reduce:
- unsupported customer-facing claims;
- manual review load for routine factual checks;
- regressions from model, prompt, or knowledge-base changes;
- incidents caused by hallucinated streamed output;
- duplicated guardrail work across LLM providers and application teams.
It can increase:
- buyer confidence that LLM output remains tied to governed facts;
- operational visibility into why an answer was allowed, rejected, halted, or routed;
- reuse of one guard policy across SDK, REST, gRPC, voice, agent, and inference-server deployments;
- quality of procurement, security, and compliance evidence.
Director-AI does not remove the need for domain experts, governance, access control, or legal review. It gives those functions a concrete enforcement point inside LLM output flow.
What Ships Publicly¶
| Surface | Public package/docs | Commercial extension boundary |
|---|---|---|
| Director-Lite | director-ai-lite, three-line guard facade, free onboarding |
None required for free-tier use |
| Director-AI | director-ai, open-core SDK, scorers, APIs, integrations, evidence packet, docs |
Paid Pro use for Advanced & Labs production deployment |
| Director-Class AI | Separate product and repository; no public PyPI package | Runtime action control, human approval routing, and tamper-evident action evidence under scoped commercial terms |
This table is the public-vs-commercial boundary; for the full commercial tier ladder and prices (Director-Lite, Director-AI, Pro, Full, Director-Class AI) see Pricing.
The public repository contains the core software and general evidence surfaces. Customer-specific sector packs, tuning datasets, deployment recipes, acceptance criteria, and performance claims must be validated against the customer's own governed data before they are used commercially. Director-Class AI integration is scoped independently when an agent also needs an effector-bound action gate.
First Evidence Packet¶
Before any serious pilot discussion, produce a small evidence packet:
pip install "director-ai[nli]"
director-ai evidence --emit evidence/
director-ai verify-evidence evidence/
That packet should prove:
- a governed fact loaded successfully;
- a grounded answer passed;
- a hallucinated or contradictory answer failed;
- the decision record has a digest and can be verified;
- the operator can explain what happened without exposing secrets.
Documentation Path¶
| Need | Read next |
|---|---|
| Product and tier boundary | Product Overview |
| Buyer value and budget language | Market Value and Positioning |
| First local run | Quickstart |
| Guided notebook path | Notebook Gallery |
| API selection | API Reference |
| Pilot checklist | Evaluation Onboarding |
| Production operation | Production Guide |