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AD-022: Brand Voice

Summary​

The brand voice subsystem keeps AI-generated and translated content on-brand. Its core type, brand.VoiceProfile, is a portable YAML document describing tone, style, vocabulary rules, examples, and locale/channel overrides. Two registered tools evaluate text against a profile: a deterministic, offline brand-vocab-check (rule-based vocabulary) and an LLM-based brand-voice-check (tone/style/clarity). Findings carry a severity and a run-anchored position and roll up into an MQM-inspired 0–100 compliance score. The kapi brand command tree (new, guide, check, rewrite, profiles, show, import, pack) exposes this as a text-first, JSON-first surface that works fully offline against a starter pack, a standalone YAML file, the local SQLite brand store, or a profile bound to a .kapi project. A small MCP surface (brand_guide, brand_check, brand_rewrite) mirrors the deterministic path for AI agents.

Context​

neokapi's positioning is to plug into an AI assistant and keep its output on-brand and consistent before publishing it in other languages and formats. A brand voice is the natural unit of that guardrail: a reusable description of how a brand wants to sound, against which a draft can be scored and rewritten. The subsystem must satisfy several constraints:

  • Portable and git-shareable. A profile is a YAML document a team can commit and review, with no backing store required — the same way a .kapi recipe is portable (AD-008).
  • Offline by default, AI-optional. A vocabulary check (forbidden, competitor, and preferred terms; regex patterns) is deterministic and needs no network. An LLM check for the subjective dimensions (tone, style, clarity) is opt-in and credential-gated.
  • Composable with the rest of the engine. Brand evaluation runs as registered tools (AD-006) so it composes into flows, reuses the schema/config machinery, and writes findings as block annotations that other tools and the UI can read.
  • Multiple surfaces. The same capability must be reachable from the CLI, the MCP server for agents, and the bundled Agent Skill (AD-024).

AD-010 handles terminology consistency at the concept level; brand voice is the broader, prose-level guardrail. The two intersect at vocabulary rules, which the vocab check can optionally cross-reference against a termbase.

Decision​

Data model — core/brand​

VoiceProfile is the canonical type. It is loaded from YAML by brand.LoadProfileYAML, the single loader used by standalone files, the embedded starter packs, and the SQLite store. Its shape:

  • ToneProfile — personality adjectives, formality, emotion, humor, and free-text guidelines.
  • StyleRules — active voice, sentence length, point-of-view, contractions, and prohibited/required regex Patterns, each with a severity.
  • VocabularyRules — preferred, forbidden, and competitor TermRules (each with an optional replacement, note, and severity), plus abbreviations.
  • VoiceExamples — before/after rewrites with explanations.
  • LocaleOverride / ChannelOverride maps — locale- and channel-specific adjustments resolved on top of the base profile.

The profile also carries versioning fields (a ProfileVersion snapshot per update, named ProfileTag references) for stores that track history.

Findings and scoring​

A finding is a brand.BrandVoiceFinding, which is a type alias to check.Finding from the framework's content-verification core (core/check). The struct carries a free-form Category string — a brand finding sets it to a brand dimension (tone, style, vocabulary, clarity, brand_compliance), modeled by the brand-local Dimension type — a Severity (neutral, minor, major, critical), a human message, an optional suggestion, the original text, optional metadata, and a Position model.RunRange — so a finding is anchored to the runs it concerns, the same run-range model used for overlays and redaction (AD-002). Severity and SeverityWeight are re-exported from core/check. Tools attach findings to a block as a BrandVoiceAnnotation (annotation type brand-voice), which also carries the profile id, the overall score, and its own Position.

This finding/severity/scoring path is shared across all checkers (terminology, do-not-translate, placeholder, register, brand), not bespoke to brand voice — brand voice is one checkset over the generic core, with BrandComplianceScore providing a dimension-shaped presentation of the generic check.Score.

brand.CalculateScore rolls findings up using the MQM-inspired penalty weights defined in core/check.SeverityWeight — neutral 0, minor 1, major 5, critical 25 — per dimension. Each dimension starts at 100 and is reduced by its penalty (clamped to 0); the overall score is 100 minus the total penalty. The dimensions are fixed (tone, style, vocabulary, clarity, brand_compliance), so a BrandComplianceScore always has a consistent shape.

The two tools​

Brand evaluation is implemented as two registered tools so it composes into flows and shares the tool schema/config machinery (AD-006):

  • brand-vocab-check (core/tools) — deterministic and offline. It scans source text for forbidden, competitor, and preferred-term violations and regex pattern hits, emitting findings with positions. It optionally takes a termbase to filter by brand vocabulary. It is an Annotate tool (read-only; writes the annotation, not the content). This is the fast first pass.
  • brand-voice-check (core/ai/tools) — LLM-backed. It asks an AI provider (AD-011) to score the subjective dimensions (tone, style, clarity) against the rendered voice guide, returning findings. It declares RequiresCredentials and an API-call side effect, produces the quality.brand-voice annotation, and runs with bounded per-block parallelism.

Both resolve their profile eagerly (supplied programmatically) or lazily through a ProfileResolver against an organizational context hierarchy, so a host can defer profile selection to runtime.

Profile sources and the kapi brand command tree​

NewBrandCmd (cli/brand.go) builds the kapi brand group. A profile is resolved from one of three mutually exclusive sources:

  • --profile <name> — a profile in the local SQLite brand store (opened with the standard --name/--local/--file resource flags, mirroring termbase and TM);
  • --profile-file <path> — a standalone, git-shareable profile YAML;
  • --pack <name> — a built-in starter pack.

With no source flag, resolution falls back to the .kapi project in scope: the recipe's defaults.brand_voice binding (a BrandVoiceBinding selecting a profile file, store profile, or pack — resolved relative to the project root), then a convention file at <root>/brand.yaml or <root>/.kapi/brand.yaml. This lets kapi brand check DRAFT.md work flag-free inside a project. Locale and channel overrides apply on top via --locale/--channel.

The subcommands:

CommandPurpose
newScaffold a commented, schema-valid profile YAML to fill in (optionally seeded from a --pack).
guide / showRender the profile as a markdown voice guide to inject into an assistant's context.
checkScore text against the profile (vocab always; --ai adds the LLM check). --min-score turns it into a gate.
rewriteRewrite text to comply — deterministic term substitution by default, full LLM rewrite with --ai.
profilesList profiles (local store + built-in packs).
importImport a profile YAML into the local store.
packInstall a built-in starter pack into the local store.

check and rewrite read their subject text from --text, a positional file, or stdin. check --min-score returns the ErrQualityGate sentinel when the score is below the threshold, which the CLI maps to a distinct exit code (AD-013) so skills and CI can tell a failed gate from an operational error.

Built-in starter packs​

The framework embeds a small set of starter packs (core/brand/packs, embedded via //go:embed *.yaml): professional-b2b, friendly-dtc, technical-docs, marketing-blog, and customer-support. Each is a complete VoiceProfile YAML, loaded through the same brand.LoadProfileYAML path as any other profile, so packs are an on-ramp, not a special case — kapi brand new --pack <name> emits one as an editable base.

MCP surface​

cli/mcp_brand.go registers offline brand tools on the shared kapi mcp stdio server (AD-013) so non-CLI agents get parity:

  • brand_guide — render a voice guide from a pack or profile YAML;
  • brand_check — score text using the deterministic vocabulary rules;
  • brand_rewrite — substitute forbidden/competitor terms (deterministic).

The MCP surface is deliberately the offline, deterministic subset (no LLM provider, no store): agents that want the AI check use the CLI's --ai path, per the CLI-vs-MCP boundary. The same file also exposes term_lookup and tm_search so an agent can enforce terminology and reuse prior translations alongside the brand checks.

Consequences​

  • A brand voice is a portable YAML document that works with or without a store, reviewable in git and reusable across the CLI, MCP, flows, and skills.
  • The deterministic vocabulary check gives an instant, offline, reproducible signal; the LLM check is a clearly bounded, credential-gated opt-in for the subjective dimensions.
  • Findings are run-anchored and annotation-shaped, so they compose with the content model and surface uniformly across tools and UIs rather than being a bespoke side channel.
  • The MQM-style scoring is a single function over findings, so every surface (CLI, MCP, a flow) computes the same 0–100 score the same way.