AI agents that do the work, built for your business, owned by you.
Production-grade multi-LLM agents for claims, underwriting, intake, dispatch, customer ops.
Not chatbots that answer questions about your software. Agents that take actions inside it. We build them, you own them, your data trains them.
Agents take actions. Chatbots answer questions.
Most products today add “AI” and they mean a chat box and a few autocompletes in the UI. Useful, but not transformational.
An agent is different. It has tools. It can call APIs. It can decide what to do next. It holds context across multiple steps. It hands off to a human when it's not sure. It's software that takes actions on your behalf, not just answers questions about your data.
Example: In our Logan trucking insurance CRM, the agent doesn't just answer “what's this customer's loss ratio?” It pulls the submission, runs it through 16 carrier appetite rules, scores each match, fills out the ACORD 25 form, routes for e-signature, and notifies the underwriter. The underwriter approves or overrides. The agent did the mechanical work. The human kept the judgment call.
That's the difference. We build the second kind.
Four shapes of production agents.
Workflow agents
Agents that orchestrate a multi-step business process end-to-end. Example: Logan's submission-to-quote flow extracts data from a PDF, matches 16 carriers, generates ACORD forms, routes for e-sig, notifies the underwriter. Human in the loop at the right moments, automation everywhere else.
Reviewer assistants
Agents that sit alongside a human worker, suggesting actions, drafting responses, flagging issues. Example: ClaimDeck's adjuster assistant. Every claim gets an AI-generated triage suggestion with reasoning attached. The adjuster accepts, rejects, or modifies. Full audit trail per decision.
Document AI
Agents that read unstructured documents (PDFs, emails, scanned forms) and extract structured data. Example: ACORD 25 / 101 generation in Logan. Agent reads insurance intake documents, pulls 80+ fields, populates the form, flags low-confidence fields for human review.
Vertical assistants
Agents trained on your industry's vocabulary and workflows. Example: a HIPAA-aware patient intake assistant we built for a telemedicine brand. Handles eligibility checks, formulation routing, state-by-state Rx rules. Lives inside their existing app, no separate UI.
The stack we ship on, in plain English.
| Layer | What we use | Why |
|---|---|---|
| AI model layer | Claude (Anthropic) for reasoning, Gemini for fast structured extraction, OpenAI as fallback | Best-of-class per task type. We route per call: Claude for narrative, Gemini for extraction. Average cost: ~$0.04 per agent invocation. |
| Orchestration | LangGraph (Python) | Graph-based agent state machines. Production-tested. Referenced in a third of Fortune-1000 architecture docs as of Q1 2026. |
| Tools / actions | MCP (Model Context Protocol) servers | Open standard for AI tool integration (Anthropic, gaining mass adoption). Lets agents talk to your existing systems without lock-in. |
| Knowledge | Custom RAG with hybrid retrieval | Vector search + keyword search combined. Your knowledge base stays on your infra. Your data never trains anyone else's model. |
| Runtime | Deployable to your AWS / GCP / Azure / on-prem / Mac Studio | Your AI runs on your hardware. No external API calls if you don't want them. |
| Audit log | Built-in from Day 1 | Every agent decision is logged: input, model output, confidence, action taken. Auditors and your team can always trace 'why did the agent pick X?'. |
Why this stack instead of Salesforce Einstein or HubSpot Breeze? Two reasons. One: per-conversation pricing on platform-native AI ($1+/resolved ticket on Breeze, opaque credit pools on Agentforce) doesn't scale once your volume grows. Two: when the agent runs inside someone else's SaaS, your data trains their model, not yours.
Custom AI vs platform AI: honest framing.
Salesforce Einstein / HubSpot Breeze wins when
- You're under 50 users and per-conversation pricing isn't yet painful.
- You only need agent capabilities for sales/support workflows your platform already models.
- You don't need to control the model, infra, or data flows.
- Your team doesn't have engineering capacity to maintain a custom system.
Custom AI agents win when
- You're scaling past 100 users + heavy AI usage.
- Your workflows are vertical-specific (insurance, healthcare, restoration, custom commerce).
- You need explainable reject paths + audit logs (regulated industries).
- You need agents that work across multiple systems, not just inside one platform.
Three systems running today.
Logan
Multi-LLM agent system for carrier matching + ACORD form generation. Matches 16 carriers in <200ms p95. ACORD 25 / 101 pre-fill from agent-extracted data. ZeboSign e-sig integration. In production.
Read the case studyFullServicePro
AI-assisted restoration claims work with explainable reject paths. Every AI decision is reviewable, every confidence score visible. Adjusters stay in control.
Read the case studyDear Brightly
State-aware checkout agent + Rx eligibility verification. Handles state-by-state prescription rules across 50 states.
Read the case studySame 4-step process across every service.
Common questions.
Want to talk about agents?
Free 30-min architecture call. We'll tell you honestly whether AI agents are the right call for your situation, or whether you're better off with platform AI or no AI at all.