Lead qualification agents with conversation memory
Task-specific AI agents that handle one job exceptionally well — research, classification, drafting, routing, and triggered actions with proper tool use, memory, and guardrails.
AI Services
AI Agents
Task-specific AI agents that handle one job exceptionally well — research, classification, drafting, routing, and triggered actions with proper tool use, memory, and guardrails.
AI Agents
Task-specific AI agents that research, route, draft, and trigger real actions.
OpenAIAnthropic ClaudeAzure AILangChainPineconeSupabaseRetell AI
Operating problem
Where this usually breaks
Sales teams needing automated lead qualification at scale
Operations teams running classification and routing work
SaaS founders productizing AI as a feature
Agencies offering AI-powered services to clients
What gets built
A system, not a one-off workflow
Task-specific AI agents that research, route, draft, and trigger real actions.
Research and competitor monitoring agents
Task-specific AI agents that handle one job exceptionally well — research, classification, drafting, routing, and triggered actions with proper tool use, memory, and guardrails.
Customer message classification and routing
Task-specific AI agents that handle one job exceptionally well — research, classification, drafting, routing, and triggered actions with proper tool use, memory, and guardrails.
Deliverables
- Lead qualification agents with conversation memory
- Research and competitor monitoring agents
- Customer message classification and routing
- Internal knowledge-base RAG assistants
- Multi-tool action agents (read CRM, draft email, log task)
- Guardrails, eval suites, and human-in-the-loop fallbacks
Best fit for
- Sales teams needing automated lead qualification at scale
- Operations teams running classification and routing work
- SaaS founders productizing AI as a feature
- Agencies offering AI-powered services to clients
Outcomes
Why teams keep this running after launch
01
Repetitive cognitive work runs without human bottleneck
02
Agents stay scoped — they do one job, well, every time
03
Guardrails and eval suites prevent hallucination drift
04
Real tool use — not chatbots, but actors that change state
Implementation
How the engagement runs
- Step 1
Discovery
Map your workflow, tools, success metrics, and constraints.
- Step 2
Design
Document inputs, outputs, edge cases, and the system architecture.
- Step 3
Build
Implementation wired into your real environment with monitoring.
- Step 4
Handover
Runbooks, documentation, and a clean transfer to your team.
FAQ
What's the difference between an AI agent and a chatbot?
Chatbots talk. Agents act. Agents read from systems (CRM, calendar, knowledge base), reason about state, and trigger real actions (book demos, update records, send messages). They have tools and goals — not just dialogue.
How do you stop agents from going off-script?
Guardrails at every level: scoped tool access, strict output schemas, confidence thresholds, evaluation suites, human-in-the-loop checkpoints, and clear failure modes. The agent does one job and only one job.
Can agents work with our existing systems?
Yes — that's the point. Agents are designed around your APIs. They read and write to your CRM, calendar, support tool, and database via service-account credentials and least-privilege access.
Ready to ship a AI Agents system?
Book a free consultation. We'll scope your workflow and decide if this is the right first build for your team.