Agent archetypes
What we actually build, in six shapes.
Every Rowbase engagement is specific to the workflow — but the underlying systems usually fall into one of these patterns. Each is built to be source-grounded, reviewable, and owned by the client at the end of the engagement.
01 · Knowledge
Source-grounded Q&A agents
Answers questions across docs, policies, contracts, and prior decisions — with citations to the exact paragraph the answer came from. The fit is internal teams who need to act on the answer, not just read it.
- Plain-English answers with line-level source citations
- Permission-aware retrieval that respects existing access rules
- Confidence gating so low-certainty answers route to a human
- Feedback loop that captures every correction
- Surface in Slack, in the existing tool, or as a clean web interface
02 · Workflow
Workflow & approval agents
Drafts the document, runs the review, or routes the approval — embedded in the system where the work already happens, with humans in the loop on anything material.
- First-pass drafts in your firm's voice and structure
- Configurable review surfaces for novel or high-stakes decisions
- Audit trail of every change, every reviewer, every source
- Integrates into existing deal, claims, or proposal workflows
- Captures reviewer edits as training signal for the next draft
03 · Copilot
Embedded internal copilots
A teammate for non-technical operators — sitting inside the tools they already use, grounded in your operating history, never a generic chatbot.
- Suggested responses, never auto-sent without review
- Grounded in your wiki, tickets, changelogs, and tribal knowledge
- Confidence-aware routing to senior teammates when needed
- Improves with every edit a teammate makes to its suggestions
- Onboarding accelerator for new hires — they ramp on real context faster
04 · Review
Document review & extraction agents
Reads an outgoing or incoming document against your operating standards and produces a structured report: anomalies, missing required elements, conflicts with recent guidance, plus the source for every flag.
- Structured, reviewable output — not free-form chat
- Severity routing: high-risk to a partner, low-risk to the author
- Versioned standards library so changes propagate cleanly
- Extracts specific fields from messy source documents reliably
- Plugs into the existing document workflow, not a separate step
05 · Assistant
Customer-facing assistants with guardrails
When the AI needs to talk to a customer, the bar is different. We build assistants that stay tightly scoped to what the business has actually decided to say, with escalation paths that work.
- Strict topic boundaries enforced at the system level
- Pre-approved response patterns for common requests
- Clean escalation to a human at any point in the conversation
- Logging and review surface for ops to spot drift early
- Brand voice tuning grounded in actual past messages
06 · Engineering
Data & evaluation engineering
Most of the work to make an AI system trustworthy is engineering, not modeling. We build the ingestion, retrieval, evaluation, and observability that turn a demo into something a serious team will use.
- Ingestion pipelines for the messy sources that matter
- Retrieval tuned to the actual questions the business asks
- Eval harnesses tied to real outcomes, not vibes
- Permissions and provenance baked in from day one
- Observability so you can see what the system is doing in production
Not sure which one fits your workflow?
Tell us the workflow you have in mind. We'll point at the archetype that fits, scope a useful first version, and tell you what we'd need from your team to ship it.