Workflow Mapping & Tool Scoping
We do not build agents blindly. We document your exact manual workflows, isolate the decision trees, and map the specific REST APIs and internal databases the agent will need to securely access.
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AI Agent Development
AI agents that get work done
We architect deterministic, tool-using agents that execute multi-step workflows, query your databases, and manage APIs under strict guardrails, not passive chatbots.
What we build
Deterministic, tool-using agents that execute real work under strict guardrails, not passive chatbots.
Chatbots generate text; agents act. We give models typed API toolkits so they read CRM data, update records, and trigger webhooks through validated function calls.
- Function Calling
- API Toolkits
- Webhooks
Specialized micro-agents that hand off work: a researcher gathers, an analyst synthesizes, an executor ships, all coordinated with LangGraph and CrewAI.
- LangGraph
- CrewAI
- Handoffs
High-stakes steps pause for approval. Before an agent sends a payment or deletes a record, it halts and routes a one-click sign-off to your team.
- Approval Gates
- Pause & Resume
- Audit Trails
Agents that scrape, ingest, and structure data around the clock, turning unstructured PDFs and pages into rows in Postgres or Snowflake.
- Web Scraping
- PDF Ingestion
- Snowflake
Agent delivery process
A disciplined, transparent path from initial scope to production handoff, so you always know what is shipping, and when.
Prompt Engineering & Routing
We write highly constrained system instructions and build semantic routers that direct user inputs to the correct specific agent, preventing generic or hallucinated responses.
State Management Configuration
Agents require short-term memory to complete complex tasks. We implement strict graph-based state management, ensuring the agent remembers context across multi-step execution paths without context-window overflow.
Testing & Sandboxed Deployment
Before touching production data, we deploy the agents into heavily monitored staging environments, running thousands of simulated adversarial inputs to ensure tool-use reliability and permission enforcement.
The agent stack
The orchestration, memory, and sandboxing layers we build on, chosen per workflow rather than by habit.
Orchestration
LangGraph and custom Python orchestrators for stateful, branching workflows, plus n8n where a visual flow is enough.
Memory & state
Graph-based state, short-term memory in Redis, and durable history in Postgres, so agents keep context across long runs.
Tools & sandboxing
Every tool call runs behind typed schemas in sandboxed containers, with strict permissions on what an agent can touch.
Tools & integrations
The systems we wire agents into, so autonomous work shows up where your team already operates.
SaaS & CRM
Connectors into CRM, billing, and ticketing so agent actions land in the systems your team already runs.
Data & warehouses
Ingestion into Postgres, Snowflake, and object storage, with caching that keeps repeat lookups cheap.
Automation & webhooks
Event-driven triggers and webhooks that let agents react the moment something changes.
Frequently asked questions about AI agent development
Pricing, process, ownership, and technical answers for AI agent development projects.
$15K to $80K for most custom builds; AI automation workflows start lower, around $8K to $20K. We don’t bill hourly. We scope a fixed price per phase, so you know the total before signing anything.
Fixed price, per phase. We break the work into scoped phases, quote each one, and hold to it. If scope changes mid-build we re-quote the remaining phases and you approve before we continue. No surprise invoices.
Book a 20-minute call. Our founder talks through your project, gives you a timeline and price range, and is straight about whether we’re the right fit. No pitch deck, no sales funnel.
An LLM (like GPT-4) is simply a text-prediction engine. An AI Agent is a software system that uses an LLM as its reasoning engine to write code, decide which internal tools to use, and execute real-world actions (like updating a Jira ticket or sending a Slack message) to accomplish a specific goal.
We utilize strict Function Calling (JSON schemas). The LLM is technically incapable of directly modifying your database. It can only output a pre-approved JSON structure requesting an action. Our deterministic middleware validates that JSON payload against your strict business rules before allowing the actual API execution.
We evaluate based on project complexity. We utilize LangChain and LangGraph for complex, stateful multi-agent workflows that require cyclical logic. For simpler execution paths, we use lightweight custom Python orchestrators to minimize latency and cloud compute costs.
Explore more services
Adjacent capabilities from the same senior team, for when your roadmap goes further.
Let's build your project
Book your free 20 minute product discovery call with us.
Looking forward to meet you.
Prefer email? [email protected]