Inject

Any data item

Connectors or approved one-off imports load email addresses, cookies, events, CRM rows, products, support, billing, documents, and outcomes.

Store

Attribution paths

Scout stores each item's relationship set, vector, and ordered attribution path in customer-owned PostgreSQL/pgvector. Fortress moves high-load memory into Rust/LanceDB, not KynticAI product operations.

Compare

Rust + LanceDB

Enterprise contains the proprietary Rust relationship, weighting, traversal, and LanceDB path for fast similarity analysis across millions of relationship sets.

Output

Top-example JSON

The engine creates JSON with the best examples, importance bands, confidence, caveats, and ranked task options for the question being asked.

Explain

LLM task brief

Fortress sends JSON to your approved model boundary. The proposed Elite path shows the executive walkthrough from safe discovery to scoped pilot and outcome review.

Clarity Gateway / Intent Framing

Turn an unclear request into a brief the model can actually answer.

Intent framing captures the buyer's real ask, removes avoidable ambiguity, and creates a governed instruction before generation, retrieval, routing, or human review begins.

Messy request in

A vague board-pack, support, legal, sales, or operations prompt is captured before it becomes expensive work.

Model-ready brief out

The output states goal, scope, source need, caveats, and format in a compact record the next system can use.

Proofable handoff

The buyer can inspect the framed intent without seeing proprietary mediation mechanics.

What it does

The concrete job

It captures the subject, action, timeframe, decision standard, source need, output shape, and unresolved variables, then prepares a concise handoff for the next system.

Why buyers care

The commercial reason

Buyers get the visible product moment: the assistant stops guessing, the task becomes crisp, and senior teams see cleaner first-pass work without stuffing every possible context into the prompt.

Sales path / Intent Framing

A vague board-pack ask becomes a model-ready renewal action brief.

This is the moment buyers feel immediately: the product takes a messy commercial request and turns it into a structured job the next system can answer without guessing.

Buyer question

What should the model do with this email enquiry, account note, or board-pack request?

Example output buyers can understand

LLM task brief: create a top-10 renewal-risk action plan using authorised CRM, support, billing, and usage signals.

Sales action: call the account owner today with support recovery steps before the renewal conversation.

Account-registration action: invite the admin to complete setup only where product use is down because onboarding stalled.

Instead of paying for a polished answer to a vague request, the buyer gets the right work packet before the model spends effort.

01

Messy request in

A senior user asks for help, but the request blends renewal risk, support recovery, product usage, and executive reporting.

prompt = look at the renewal stuff before the board pack

signals = renewal soon, support open, product use down

risk = model could summarise instead of drive action

02

Intent framed

Clarity pins the decision, the scope, the evidence needed, and the output shape before downstream work starts.

goal = rank renewal-risk accounts

scope = next 45 days

sources = CRM + support + billing + product usage

03

Clean handoff out

The downstream model or workflow receives a clearer, sharper task with caveats attached.

intent_frame = renewal-risk action brief

output_shape = top 10 accounts + owner action

caveat = confirm board-pack cut-off date

Clarity example in plain English

Intent framing in plain buyer terms.

The buyer can see the motion without proprietary internals: a confusing request enters, Clarity resolves the task shape, and a cleaner handoff leaves for the next model, workflow, tool, or human owner.

In

The buyer brings a confusing moment

The input is the prompt, request, handoff, workflow step, or support question that usually creates rework.

Read

Clarity reads the task shape

It captures the subject, action, timeframe, decision standard, source need, output shape, and unresolved variables, then prepares a concise handoff for the next system.

Fix

The ambiguity is resolved

The page-specific capability either asks the useful question, frames intent, stores safe session scope, builds a handoff, or prepares the integration route.

Out

A usable handoff comes out

The output is buyer-readable: a question, structured brief, safe envelope, proof marker, route hint, or action-ready note.

Example input

A vague board-pack request becomes an answerable renewal-risk brief

User: 'Can you look at the renewal stuff and tell me what to do about risky accounts before the board pack?'

Context: CRM, support, billing, and product-usage sources are authorised for this workflow.

Missing variable: the user still needs to define risky and the look-ahead window.

Example output

intent_frame = rank renewal-risk accounts for board review

scope = accounts with renewal events in the next 45 days

source_need = CRM + support + billing + product usage

output_shape = top 10 action brief with caveats and owner-ready next steps

unresolved = confirm board-pack cut-off date when the workflow needs it

Buyer result

Buyers get the visible product moment: the assistant stops guessing, the task becomes crisp, and senior teams see cleaner first-pass work without stuffing every possible context into the prompt.

Concrete example

A vague board-pack request becomes an answerable renewal-risk brief

Example input

User: 'Can you look at the renewal stuff and tell me what to do about risky accounts before the board pack?'

Context: CRM, support, billing, and product-usage sources are authorised for this workflow.

Missing variable: the user still needs to define risky and the look-ahead window.

Example output

intent_frame = rank renewal-risk accounts for board review

scope = accounts with renewal events in the next 45 days

source_need = CRM + support + billing + product usage

output_shape = top 10 action brief with caveats and owner-ready next steps

unresolved = confirm board-pack cut-off date when the workflow needs it

Proof marker

Deterministic intent-framing snapshot for the same resolved request.

Public output contains the compact intent record while proprietary mediation internals stay protected.

Source and caveat fields stay attached so the next workflow knows what still needs checking.

How it works

The operating flow buyers can understand.

Each Clarity capability is explained through input, output, route, and validation markers. The proprietary method stays protected while the buyer sees exactly what the system creates and why it matters.

Find

Read the real request

Separate what the user typed from the decision they are trying to make.

Pin

Lock the task shape

Identify goal, subject, timeframe, source need, and output format before generation starts.

Frame

Structure the handoff

Forward a concise intent record instead of a long prompt tail.

Carry

Keep caveats visible

Preserve missing fields and source expectations so the next step stays honest.

Output created

The data artefacts that make the capability useful.

Structured intent record

A compact instruction with goal, scope, source need, output shape, caveats, and route hints.

Model-ready context plan

A cleaner prompt target for the approved model, proxy, SDK call, or internal workflow.

Audit-friendly trail

A buyer-readable reason the request was reframed and what the next system received.

Buyer payoff

The model stops chewing through vague prompt baggage.

Pain

A high-value model receives a long, emotional, half-specified request and writes a polished answer to the wrong target.

Relief

Clarity sends a compact intent record with the exact decision, scope, sources, caveats, and output shape.

Outcome

Less rework, cleaner handoffs, and better first-pass answers in executive, support, sales, legal, and operations workflows.

Proof

Proof is grounded in repeatable input/output snapshots and safe handoff scans rather than benchmark claims.

Example scenario boxes

Where this capability shows up in the real buyer conversation.

Executive brief

Old way

The board-pack request becomes a generic account summary.

With Clarity

The request is framed as renewal-risk ranking with source and timeframe pinned.

The executive receives an action brief instead of a broad narrative.

Support escalation

Old way

The assistant over-answers with every known support fact.

With Clarity

Intent framing sends only the complaint, entitlement, impact window, and desired response shape.

The support owner gets the next action with the prompt noise already stripped away.

Operations triage

Old way

A vague operations request creates a blended answer across stock, fulfilment, and customer risk.

With Clarity

The intent frame isolates the operational decision and the source fields needed.

The workflow routes to the right team with the right evidence request.

Continue the product path

Connect this capability to the rest of KynticAI Clarity Gateway.

Bring the repeated ambiguous request. KynticAI will shape the first Clarity proof around it.

Pick the repeated prompt, workflow, support path, agent route, or executive brief where one missing variable causes expensive rework. KynticAI can map the first Clarity proof around that moment.

Evidence Results

Clarity Gateway clears intent before provider routing.

These examples show how resolved intent, routing, and safer handoffs improve model output.

KynticAI Result
Intent Clarity

Clarity scenario - vague prompt, missing timeframe, missing account scope

Stop answering the wrong question

Before an ambiguous request reaches the configured provider, Clarity Gateway asks the one useful question that pins the subject, timeframe, data source, or output shape.
KynticAI Result
Agent Routing

Clarity scenario - model, tool, Context Engine path, human review

Send the work to the right path

Resolved intent can route to Scout, Fortress, KynticAI Importance, an approved model endpoint, a tool, or a human workflow.
KynticAI Result
Instruction Fidelity

Clarity scenario - structured request, cleaner handoff, fewer retries

Reduce ambiguous provider calls

A clarified request can be framed as a cleaner instruction before generation begins.