Product / KynticAI Context Engine
Context Engine turns authorised company data into relationship JSON the business can act on.
Context Engine is the product family for Scout, Fortress, and the proposed Elite path. It creates source-traced relationship context from approved company data, then hands governed JSON to the right model, workflow, or human team.
Same engine family, three buying paths: prove it with Scout, private-deploy it with Fortress, and use proposed Elite for executive walkthroughs where approved.
Product selector
Pick the Context Engine option by where the buyer is in the journey.
The menu stays simple, but the buying path stays precise. Scout is the free proof path. Fortress is the private runtime. Proposed Elite is the executive walkthrough route around Discovery MCP, synthetic demo, Fortress scope, and outcome review.
Free open-source entry point
Scout
Teams that want to prove the Context Engine pattern locally before buying the private enterprise runtime.
Authorised data items become relationship sets, attribution paths, source trails, and governed top-example JSON.
Run a first-source proof with PostgreSQL/pgvector, inspect the JSON, and see the task shape before enterprise rollout.
Private enterprise runtime
Fortress
Enterprises that need the Context Engine pattern inside customer-controlled infrastructure with stronger scale, governance, and support boundaries.
Private connectors, Rust/LanceDB relationship analysis, governed JSON, and customer-approved model handoff stay inside the deployment boundary.
Move from Scout proof to private runtime when volume, concurrency, governance, or enterprise ownership matters.
Executive walkthrough path
Proposed Elite
Leadership teams that want to see Discovery MCP, synthetic equivalent demo, Fortress pilot scope, and outcome-review planning in one route.
The proposed path packages local discovery, metadata-only signature approval, synthetic demo, Fortress bridge, and optional local explanation routing where approved.
Use it when the buyer needs an executive walkthrough before deciding whether Fortress pilot scope is the next commercial step.
Data in, data out
Context Engine is the business-signal product: source data in, next-task context out.
A new buyer should understand Context Engine before they understand Scout, Fortress, or proposed Elite. It takes approved business signals, joins them into relationship context, and returns a source-traced brief for the next action.
Business signals enter
Email, CRM, support, usage, billing, product, web, document, and outcome signals enter through an approved route.
KynticAI links the relationship
The system connects those signals to the account, customer, case, event, product, or workflow moment being analysed.
Similar journeys are found
Known converted, retained, lost, escalated, or delayed examples are compared so the next task is not guessed from isolated records.
A JSON task brief comes out
The output shows the strongest examples, source trail, caveats, and next action for the chosen model, team, or workflow.
Example input
Buyer enquiry
email = buyer@example.com
event = product enquiry
web path = pricing page -> integration page
CRM status = open opportunity
support signal = no open blocker
similar outcomes = converted accounts with the same path
Example output
context_engine.object = buyer enquiry
top_example = previous enquiry with same product path and conversion outcome
task_option = send technical follow-up with integration proof
confidence = evidence-supported band
caveat = confirm procurement owner before proposal
handoff = governed JSON for approved model or human workflow
Buyer result
The buyer sees how disconnected business signals become a source-traced next action the sales team can understand.
How it works
The same core motion across Scout, Fortress, and proposed Elite.
Context Engine does not start by asking a model to guess. It organises authorised data into relationship context first, then gives the chosen model or workflow a governed JSON brief.
Inject authorised data
Customer-approved records, events, emails, cases, products, usage, billing, and outcome signals enter the customer-owned data plane.
Build relationship context
The system keeps attribution paths, source trails, vectors, caveats, and relationship facts attached to the object being analysed.
Return top-example JSON
The Context Engine returns governed JSON that shows the strongest examples, provenance, confidence, caveats, and task options.
Explain through the chosen model path
Fortress can hand JSON to the customer's approved model boundary. The proposed Elite path can discuss local/private explanation routing where approved.
Concrete example
A buyer enquiry becomes a source-traced next action instead of another generic summary.
Example input
email = buyer@example.com
event = product enquiry
web path = pricing page -> integration page
CRM status = open opportunity
support signal = no open blocker
similar outcomes = converted accounts with the same path
Example output
context_engine.object = buyer enquiry
top_example = previous enquiry with same product path and conversion outcome
task_option = send technical follow-up with integration proof
confidence = evidence-supported band
caveat = confirm procurement owner before proposal
handoff = governed JSON for approved model or human workflow
Buyer spark
The payoff is immediate: the model receives context with provenance, not a pile of disconnected records.
Context Engine gives buyers a clean mental model and a clean buying path. Start with the free proof, move to the private runtime, then operate the value loop when the first workflow is worth scaling.
Scout makes the pattern inspectable.
Fortress makes the pattern private and enterprise-ready.
Proposed Elite makes the executive walkthrough concrete.