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Contextual AI

#481 most-used

Enterprise RAG, reranking, and LLM evaluation

DocumentsAnalyticsDeveloperAIAutomation

Contextual AI is an enterprise retrieval-augmented generation (RAG) platform that brings production-grade document Q&A, reranking, and LLM evaluation into your AI stack. Connect it to Actionist and your agents can query enterprise knowledge bases, rerank retrieval results for precision, evaluate LLM response quality with LMUnit, and route tasks to the right model — all driven by the data already living in your documents.

Try Contextual AI with ActionistVisit Contextual AI
Average time saved
14 hours
per person · per month
≈ 2 workdays back

Eliminates manual work. Agents eliminate the manual cycle of document search, copy-pasting policy clauses, checking LLM output quality by hand, and logging into the Contextual AI dashboard to manage corpus ingestion and system prompts.

Schedule

What your Contextual AI agent runs on autopilot

A week of scheduled jobs your Actionist agent will execute on your behalf.

28Scheduled jobs
7Agents at work
24/7Always on
Agents
Wed–Fri
Wed
Thu
Fri
7a
8a
9a
10a
11a
12p
1p
2p
3p
4p
5p
6p
Multi-app workflows

Contextual AI × every other app you use

End-to-end automations that span multiple apps — each one a real business outcome.

6Workflows
8Apps spanned
~74 hrsSaved / week
4Personas served
For operations
Featured3 apps

Document Q&A agent for enterprise knowledge bases

When a team member posts a question in the #ask-ai Slack channel, the agent queries the relevant Contextual AI Knowledge Agent, retrieves a grounded answer with source citations, evaluates its confidence with LMUnit, and posts the answer back to the Slack thread — with citations attached and a confidence indicator.

~20 hrs

Time saved for your team — every week, on autopilot

The flow
Trigger·When a question is asked in a Slack thread tagged #ask-ai
Trigger
Step 1
Slack
Detect question in #ask-ai channel
read
Step 2
Contextual AI
Query Knowledge Agent with the user's question
write
Step 3
Contextual AI
Evaluate response quality with LMUnit
write
Step 4
Slack
Post grounded answer with citations and confidence score
write
Step 5
Google Sheets
Log question, answer, and quality score to Q&A tracker
Result
Evaluate response quality with LMUnitPost grounded answer with citations and confidence scoreLog question, answer, and quality score to Q&A tracker
The win
Saved per run
15 min
Runs / week
~80×
Every team question gets a grounded, cited answer in seconds
Driven byOperations Agent
ROI

Savings

What your team gets back — two angles: what you stop doing manually, and what that's worth.

Without Actionist

What you do manually today

With Actionist

What your agent runs for you

  • Sales
    60 min / week
    Manual document search before calls

    Reps search Google Drive, Confluence, and shared folders for contract terms and spec details — 15 minutes of prep per call that often surfaces the wrong version.

    Sales Agent
    0 min
    Agent queries Knowledge Agent and delivers cited answers

    Before each call the agent submits the rep's questions to the product and contracts Knowledge Agent and returns grounded answers with source citations — under 30 seconds per query.

  • Marketing
    90 min / week
    Copy accuracy checked manually by a subject matter expert

    A product marketer manually reviews every piece of campaign copy for factual accuracy before it goes live — a bottleneck that delays campaigns when the expert is unavailable.

    Marketing Agent
    0 min
    Agent evaluates copy accuracy with LMUnit before the review queue

    Campaign copy is submitted to LMUnit against the 'grounded in current product specs' criterion before it reaches human review — only failing copy requires expert attention.

  • Customer Support
    200 min / week
    Agents search knowledge base manually for each ticket

    Support agents keyword-search the help centre for each ticket, read multiple articles to find the relevant passage, and write a response — averaging 8 minutes of research per ticket.

    Customer Support Agent
    0 min
    Agent reranks candidates and queries Knowledge Agent for grounded answers

    The agent retrieves candidates, reranks them, queries the Knowledge Agent, and surfaces a cited answer in seconds — the support agent verifies and sends rather than researches from scratch.

  • Human Resources
    120 min / week
    HR manually answers policy questions from employee inbox

    The HR team fields dozens of repetitive policy questions weekly — each requiring a search through the policy folder, a read of the relevant section, and a carefully worded reply.

    Human Resources Agent
    0 min
    Agent queries HR Knowledge Agent and posts cited policy answers

    The HR agent queries the policy corpus for common questions and posts grounded, cited answers to the Slack channel — employees get policy answers with the source document linked.

  • Finance
    80 min / week
    Analysts manually check contracts for payment clause details

    Finance analysts open contract PDFs, search for payment terms, and transcribe the relevant clause into a summary — taking 20 minutes per contract and prone to transcription errors.

    Finance Agent
    0 min
    Agent queries contracts Knowledge Agent for grounded clause extracts

    The finance agent queries the contracts Knowledge Agent with specific payment questions and receives the exact clause text with citation — verified in seconds, not minutes.

  • Operations
    120 min / week
    Corpus management done manually through the Contextual AI dashboard

    Operations manually ingests new documents, checks indexing status, deletes expired files, and updates Knowledge Agent system prompts via the web UI — up to 2 hours per week.

    Operations Agent
    0 min
    Agent manages ingestion, status checks, deletions, and prompt updates automatically

    The operations agent handles the full corpus lifecycle — ingestion, status polling, deletions, freshness audits, and system-prompt updates — without a human touching the Contextual AI dashboard.

  • Legal
    150 min / week
    Lawyers review AI-drafted summaries from scratch

    Every AI-drafted legal summary goes through a full attorney review regardless of quality — bottlenecking the legal team on documents that a quality gate would have cleared automatically.

    Legal Agent
    0 min
    Agent evaluates summaries with LMUnit before attorney review queue

    AI-drafted legal summaries pass through LMUnit evaluation before reaching the attorney queue — only documents that fail the quality criterion require attorney attention, reducing review volume.

+ 100s of other Contextual AI automations
Average time saved
82 hrs / person / month
Calculator

Calculate what your team saves

Team size
12 people
Hourly rate
$45 / hr
Hours saved / week
42
Hours saved / year
2,100
Annual ROI
$94,500

Based on Contextual AI's typical team usage — the visible tasks plus a few other automations the agent runs: ~3.5 hrs / person / week of admin work automated.

Connect

How to plug Contextual AI into Actionist

Pick the connection method that suits your environment.

Connect Actionist to Contextual AI using your account API key. Generate the key in the Contextual AI console and paste it into Actionist to enable all Knowledge Agent, Reranker, and LMUnit operations.

1
Open the Contextual AI console

Log in at app.contextual.ai, go to Settings → API Keys, and click Generate New Key.

2
Copy your API key

Copy the generated key immediately — it will not be shown again. Store it in a secrets manager, not in plain text.

3
Paste into Actionist

Find Contextual AI in the Actionist Apps tab, click Connect, paste your key into the API Key field, and click Test connection.

Credentials you'll need
API Key*
Contextual AI Console → Settings → API Keys → Generate New Key
Actions

13 actions your agent can call

Read and write operations available to your Actionist agent.

FAQs

Questions about Contextual AI + Actionist

How does Actionist connect to Contextual AI?
Go to the Apps tab in Actionist, find Contextual AI, and click Connect. You will be prompted for your Contextual AI API key. Generate it in the Contextual AI console under Settings → API Keys. Once you paste the key and click Test connection, Actionist runs a read-only request to confirm the handshake before any actions run. The key is stored encrypted and never exposed in logs.
What is a Knowledge Agent in Contextual AI and how does Actionist use it?
A Knowledge Agent is a Contextual AI component that combines a document corpus (a datastore of your enterprise files) with a retrieval pipeline and a language model to answer natural-language questions with citations from your own documents. Actionist agents can query a Knowledge Agent, ingest new documents into its corpus, delete outdated ones, update its system prompt, and check the indexing status of uploaded files — managing the full lifecycle programmatically.
What is LMUnit and how does Actionist use it for quality control?
LMUnit is Contextual AI's LLM evaluation framework. Given a prompt, a model response, and an evaluation criterion, LMUnit returns a quality score and explanation. Actionist agents submit AI-generated content to LMUnit before it reaches end users — checking for factual grounding, tone compliance, legal accuracy, or any custom criterion you define. You can run single evaluations inline or submit batch evaluation jobs for overnight processing and retrieve results the next morning.
What is the Contextual AI Reranker and when should I use it?
The Contextual AI Reranker is a model that takes a query and a list of candidate text passages and returns them ordered by relevance precision. It is designed to sit on top of vector search: your vector database retrieves a broad candidate set (20–100 passages), the Reranker orders them by true relevance, and only the top-ranked passages enter the LLM prompt. This pattern improves answer quality, reduces hallucination risk, and cuts token cost by narrowing context. Actionist supports this as a native action in your RAG pipeline.
How quickly are documents available for querying after I ingest them?
Document availability depends on corpus size and file complexity, but most documents are indexed and queryable within about a minute after ingestion. Actionist agents can poll the Get Document Status action after ingest and hold the workflow until the status shows indexed — so downstream query steps only run when the document is actually available. Large batch ingestions may take longer; Actionist handles the wait automatically.
Can I have multiple Knowledge Agents for different teams or use cases?
Yes. Contextual AI supports multiple Knowledge Agents in a single account, each with its own document corpus and system prompt. Actionist can list all your Knowledge Agents, create new ones on demand (for example when a new enterprise customer signs), update their system prompts independently, and route incoming queries to the right agent based on topic. Each team — sales, HR, legal, support — can have its own isolated knowledge base.
Can Actionist run LMUnit evaluations on a schedule rather than inline?
Yes. For high-volume use cases where you do not need an inline quality gate, Actionist agents can create an LMUnit evaluation job for a batch of responses and retrieve the results later. A common pattern is to submit the day's production responses as a nightly batch job and retrieve results the next morning. The Get Evaluation Results action fetches completed job results, and the agent can post a quality summary to Slack or write it to a dashboard without any human intervention.
What permissions does the Contextual AI API key need?
The API key used with Actionist needs read and write access to the resources your agents will use: Knowledge Agents (create, query, update, delete), Datastores (create, list, manage documents), and LMUnit (create evaluation jobs, retrieve results, submit inline evaluations). Contextual AI API keys are account-scoped by default. If your organisation uses scoped keys, ensure the key covers the Knowledge Agent IDs and datastore IDs your agents will interact with.
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