Your brand,
machine-readable
and always true.

Brandstract ingests fragmented brand signals and abstracts them into a structured, executable runtime — powering every AI workflow, agent, and creative tool with the brand context they need.

See how it works

brand_signature.essence extracted — confidence 0.91 tone_matrix.formal_casual → casual-conversational truth_score.completed — alignment index 0.74 — grade B runtime.locked — coherence index 0.88 signal fetched: instagram_captions — 20 posts sampled gap detected: value_proposition — severity medium webhook delivered: truth_score.completed → agency-cms mcp resource served: brand_executable — 3 consumers brand_signature.essence extracted — confidence 0.91 tone_matrix.formal_casual → casual-conversational truth_score.completed — alignment index 0.74 — grade B runtime.locked — coherence index 0.88 signal fetched: instagram_captions — 20 posts sampled gap detected: value_proposition — severity medium webhook delivered: truth_score.completed → agency-cms mcp resource served: brand_executable — 3 consumers

The problem

AI tools have no idea
who your brand is.

Brand lives in PDFs no one reads

Guidelines, decks, Notion wikis, tribal knowledge. None of it is machine-readable. Every AI tool starts from zero.

Hallucinated brand voice at scale

Without brand context, AI generates generic output that sounds like every other brand. The more you scale, the further you drift.

No single source of truth

Marketing, product, agencies, and AI tools all work from different versions. Consistency is accidental, not architectural.

You can't measure brand integrity

There's no systematic way to know whether what the brand says externally matches what it claims to be internally.

How it works

From scattered signals
to executable truth.

Ingest signal layer

Scrape your website, social channels (TikTok, Instagram, X), and uploaded brand documents up to 10 MB. Large documents are processed via chunked extraction — no truncation.

Abstract Schema v2.0

Underlying models extract strategic, creative, and tonal attributes into a structured Brand Runtime — six sections, per-field confidence scores, and full source provenance. Every value traces back to real evidence.

Review field-level

Accept, reject, or edit any extracted field. Version history, clone and restore, and a three-component coherence index computed on lock — completeness, confidence, and LLM consistency pass.

Score truth score

Measure how well your external voice matches your locked internal truth across five weighted dimensions. Real signal evaluation, no synthetic scores. Gap report with specific evidence and remediation recommendations.

Execute prompt engine

Generate platform-native creative ideas and production-grade AI video/image prompts using the Universal Effectiveness Schema — compiled for Sora, Veo, Kling, Runway, or Imagen.

Integrate API + MCP

REST API v2.0.1 with schema versioning, webhooks, and per-field provenance. MCP server for direct AI agent integration. Python and TypeScript SDKs. Every response includes extraction confidence and source attribution.

Read the technical breakdown →

Brand Truth Score

Is what you say
what you actually do?

Lock your brand runtime as canonical. Brandstract then scans your live external signals — website, social channels, owned media — and evaluates each one against your stated identity using structured rubrics.

The result is an alignment index with a letter grade, a gap report with specific evidence, and a drift trend across runs. Every score is LLM-evaluated from real content — no synthetic values.

High severity · Value proposition

Stated promise: "we help teams ship faster without sacrificing craft". Homepage headline reads: "Design tools for modern teams" — omits speed and craft tension entirely.

Medium severity · Tone & voice

Stated tone: casual-conversational, never corporate. Three LinkedIn posts in sample used passive voice inconsistent with the active, direct register in the runtime.

Acme Studio

Alignment index · 0.712

↑ Improving

Tone & Voice (30%)0.82

Value Proposition (20%)0.51

Audience Targeting (20%)0.74

Distinctive Assets (15%)0.78

Voice Consistency (15%)0.69

Model Context Protocol

Brand context,
served to any AI agent.

The Brandstract MCP server exposes your locked brand runtime as structured resources consumable directly by AI agents, visual models, and creative tools — without a developer intermediary.

Connect once. Every downstream agent inherits the brand context automatically.

Endpointhttps://app.brandstract.ai/mcp

TransportServer-Sent Events (SSE)

Tested withAI assistants · IDEs · direct SSE consumers

Sandbox keybst_sandbox_* — test without an account

SIG

brand_signature

Essence, mission, promise, value proposition, belief system, distinctive assets

EXE

brand_executable

Creative rules, tone matrix, visual language, deployment contexts — ready to constrain AI output

VIS

visual_brief

Compiled brief optimised for image and video generation models

IDE

brand_ideas

Latest locked creative ideas for the active brand runtime

Your brand,machine-readableand always true.

AI tools have no ideawho your brand is.

From scattered signalsto executable truth.

Is what you saywhat you actually do?

Brand context,served to any AI agent.

Five foundations ofAI brand infrastructure

Your brand,
machine-readable
and always true.

AI tools have no idea
who your brand is.

From scattered signals
to executable truth.

Is what you say
what you actually do?

Brand context,
served to any AI agent.

Five foundations of
AI brand infrastructure