Contextual AI builds grounded search and technical knowledge agents

Contextual AI helps organizations answer questions using their own documents and turn that knowledge into specialized agents. Its offer combines a managed retrieval platform with document parsing, reranking, generation and evaluation APIs that teams can adopt separately. The useful starting point is a difficult knowledge task: a support engineer reconciling a device log with a technical specification, for example. Buying a more fluent chatbot alone does not solve that problem if the relevant table, revision or permission boundary is missing.

01 / ProductA retrieval platform with replaceable components

The platform overview describes agents that work across specialized organizational knowledge. Retrieval-augmented generation, or RAG, means finding relevant material before composing an answer. Contextual adds tools for preparing that material, selecting useful passages and evaluating the resulting response. Those stages address different failure modes: an unreadable diagram is a preparation problem, while choosing an obsolete manual is a retrieval problem.

Agent Composer combines search, models and external tools into workflows. It supports fixed steps alongside agentic research. The current quickstart draws an important access boundary: Basic Search and Agentic Search templates are available to self-serve users, while custom workflows through prompts, the visual builder or YAML are in public preview for enterprise users. A diagram of a sophisticated agent should therefore not be mistaken for a self-serve entitlement.

The separate APIs offer a smaller adoption decision. An engineering team can try a different parser or reranker without replacing its entire application. That route preserves responsibility for the remaining pipeline, including the user interface and the connection between a retrieved passage and the final answer.

02 / AudienceFor knowledge that demands more than a plausible summary

Consider a manufacturer whose field engineers search specifications, revision notices and incident reports. Two passages may discuss the same component while applying to different firmware versions. A useful assistant must identify the applicable version and show why a suggested troubleshooting step follows. Contextual is relevant when those distinctions are frequent enough to justify a deliberately maintained knowledge application.

The fit is weaker when most questions already resolve through a short, well-maintained FAQ. Adding an elaborate retrieval pipeline may make that workflow harder to operate without improving the answer. It is also a poor substitute for assigning an owner to conflicting source documents. No retrieval configuration can determine which policy the organization intended to approve when the source record itself is unresolved.

Our Glean blueprint explores a broader employee knowledge experience across connected applications. The LlamaIndex blueprint is useful when developers want to compose document and agent infrastructure themselves. Contextual sits in that decision space with both a managed offer and individual APIs; compare how much application behavior your team wants to own.

03 / WorkflowProposed workflow for a technical support evidence packet

This is a proposed evaluation, not a test performed by Sequenced. Choose one device family and a limited set of approved manuals, service bulletins and resolved tickets. Ask engineers to supply representative questions with the source passages they would use. Include a question where the right answer is that the available evidence does not support a diagnosis.

First inspect document preparation. The Parse guide documents asynchronous jobs and outputs including document Markdown, page Markdown and structured page blocks. It also describes hierarchy metadata and table splitting that retains header context. For a service manual, verify that a tolerance value stays associated with its units, model and table heading. A clean-looking paragraph is insufficient if the numerical relationship was lost.

Next, configure retrieval around the actual decision. The rerank guide shows instructions that prioritize source types and recency when documents conflict. In this evaluation, prefer the approved revision while preserving older material needed for older devices. Keep that priority separate from access controls: ranking a private document lower does not make it safe to expose.

Use a self-serve search template for the initial question-answering stage. If the task needs a custom sequence that parses a log, branches on an error and calls another system, first secure the enterprise preview access documented for Agent Composer. Ask the response to separate observed facts, possible causes and missing evidence, with a source for each proposed investigation step.

Finally, review the evidence packet with the engineer who owns the case. Count unsupported steps, wrong-version retrievals and useful abstentions, alongside time spent checking citations. A shorter answer that identifies one defensible next investigation can be more valuable than a long explanation combining incompatible manuals.

04 / PricingPrice components separately from the complete application

Contextual’s billing guide describes On-Demand as usage-based with $25 in introductory credits and no minimum commitment, while Enterprise adds negotiated arrangements. The component rates below are not a universal price for a completed support answer. A workflow may parse documents, retrieve repeatedly, generate text and run evaluations before it finishes.

For illustration, processing 10,000 pages at the displayed Standard Parse rate is $400 for that parsing step. This arithmetic excludes generation, other processing and your engineering work. Estimate reprocessing as well as first ingestion: changing source files, parser settings or chunk strategy may create another workload. The pricing page also distinguishes one self-serve workspace from enterprise arrangements; do not assume all administrative controls are included in an API experiment.

05 / DistinctionsSource priority and evaluation can be designed explicitly

The compelling distinction is the ability to work on specific stages of grounded answers. A parser can improve table structure; a reranker can prefer an authoritative document; an evaluator can flag a response that ignores the question. Those are different interventions, and measuring them separately helps prevent a model change from becoming the default response to every problem.

LMUnit supports evaluations expressed in natural language. An engineering team could propose checks such as whether an answer identifies the relevant product revision or distinguishes a confirmed failure from a hypothesis. Treat those checks as an additional signal. A model evaluating another model’s answer is not the engineer who knows whether the recommended procedure is appropriate.

The connector entitlements guide says inaccessible source documents are excluded from generation and displayed as private in the datastore. That is a concrete behavior to verify with two test users. It is more useful than accepting a broad statement that the platform is permission-aware without checking the connector and identity path used by the application.

06 / QuestionsResolve access, refresh and evidence quality before expansion

Start with availability. If the business case depends on a custom Agent Composer graph, confirm the enterprise preview arrangement before building a schedule around it. Ask which interfaces and components are covered, what support applies and whether the configuration can be moved into the intended production environment. Public documentation can describe a capability without granting it to every account.

Then test changes in the knowledge base. Replace a manual revision and remove access for one user; check both the returned answer and the linked evidence. A correct result yesterday does not establish that a refreshed index and a changed permission behave correctly today. Keep an explicit expected result for each scenario, including whether the assistant should decline to answer.

Finally, compare evaluation signals with expert review. A response can be faithful to an outdated source, and a technically relevant passage can still omit a crucial exception. Preserve source version, retrieval context and the engineer’s correction so failures can be assigned to preparation, selection or interpretation rather than lumped together as inaccurate AI.

07 / DecisionChoose the smallest useful Contextual evaluation

A good first result is a measurable improvement in one difficult knowledge task with an understood access and cost boundary. Expand only when the evidence packet helps the responsible specialist reach a better-supported decision. The platform’s breadth becomes useful when each added stage has a clear job.