Question 1

What exactly is Dactic?

Accepted Answer

Dactic is a contextual middleware layer for autonomous agents. It fuses structured repository data with tacit team knowledge into a single, continuously refreshed context graph your agents run against.

Question 2

What is the '80% efficiency gap'?

Accepted Answer

Off-the-shelf agents complete about 80% of a task on their own. The remaining 20% — the judgement, the exceptions, the unwritten rules — is where humans still have to step in. Dactic closes that gap by grounding agents in your real operating context.

Question 3

What can I actually build with it?

Accepted Answer

Any specialized workflow agent your team already runs manually: triage, review, drafting, routing, back-office ops. You compose from pre-configured agents or design bespoke ones from your own tacit-knowledge context.

Question 4

How is this different from a consulting workflow study?

Accepted Answer

A consulting study ships a slide deck once, then rots. Dactic ships functional agents, continuously refreshes the underlying context, and costs a fraction of a traditional engagement.

Question 5

How does onboarding work?

Accepted Answer

Four steps: workspace integration, synthesis and reverse-engineering of your SOPs and data, focused interviews with the humans who own the work, and one-click deployment into your workspace.

Question 6

How long is the introductory interview?

Accepted Answer

Interviews are short and focused — typically 30 minutes per role — and target only the tacit knowledge that documents don't hold.

Question 7

Who is this built for?

Accepted Answer

Non-technical operational leaders who need business-aware agents shipped without dedicated engineering. Data and security teams stay in the loop through governance controls.

Question 8

What does implementation include?

Accepted Answer

Discovery, source integration, synthesis, interviews, and the first live agents — scoped so first value ships inside the onboarding cycle, not a quarter later.

Question 9

How is this different from standard RAG?

Accepted Answer

RAG retrieves chunks from documents. Dactic builds a graph knowledge layer over both documents and tacit context, prunes token-wasteful noise, and delivers only the context an agent needs for a given task.

Question 10

How does Dactic handle large or messy documents?

Accepted Answer

A supervisor agent orchestrates specialized sub-agents that segment dense assets and work in parallel, so long-form context never blows a single window.

Question 11

Which AI models does it work with?

Accepted Answer

Dactic is harness-agnostic. The context it builds is delivered in an open, portable format that works with any leading model or agent framework you already use.

Question 12

What are 'Goal' and 'Loop'?

Accepted Answer

Goal is the outcome an agent is grounded to achieve. Loop is the continuous refresh cycle that re-mines evidence and re-grounds every live agent against your current operating reality.

Question 13

Where is our data stored?

Accepted Answer

Your knowledge base lives on Dactic's own secure infrastructure rather than scattered across client file systems. Dedicated VPC and data-residency options are available on Enterprise.

Question 14

Is Dactic secure enough for enterprise?

Accepted Answer

Yes. Governance and audit tooling, SSO/SCIM, VPC isolation, and immutable audit logs are all available for regulated workloads.

Question 15

What is refresh cadence and why does it matter?

Accepted Answer

Refresh cadence is how often Dactic re-mines evidence and re-grounds your live agents — 30-day, 14-day, 7-day, or continuous. It's what prevents context rot.

Question 16

What counts as an 'employee under management'?

Accepted Answer

Any team member whose tacit knowledge and workflow are represented in the Dactic context layer. It's how we price to value delivered, not raw usage.

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