CodesSavvy builds custom AI agents for SaaS products. Autonomous agents that take actions inside your app — customer support agents, operations agents, research agents. Function calling, tool use, multi-step reasoning. Production-ready in 6 to 12 weeks. Fixed price.
AI Agents for SaaS
That Do the Work
Not chatbots that answer questions. Agents that read your database, call your APIs, trigger workflows, and complete tasks end-to-end. We design, build, and deploy custom AI agent systems inside your existing SaaS — integrated with your auth, your data, and your billing.
Get the Vocabulary Right First
Agent vs Chatbot vs Workflow
These three get used interchangeably in marketing and they should not be. Each one solves a different problem. Picking the wrong category is how AI projects fail before they start.
Chatbot
Answers questions
A chatbot replies in text. It can be smart, context-aware, even helpful. But it does not act on your systems.
Example: tells the user how to refund themselves.
AI Workflow
Follows fixed steps
A workflow runs deterministic steps with LLM calls embedded. Predictable. Cheap. Best when the task always looks similar.
Example: every refund follows the same 4 steps in order.
AI Agent
Decides and acts
An agent reasons about the current state, decides what to do next, and uses tools (your APIs) to act. Handles ambiguity.
Example: investigates the refund request, decides if approved, processes it, emails the customer.
Our rule: if the task has a fixed shape that does not vary, build a workflow — it is faster, cheaper, and more reliable. If the task requires reasoning over ambiguous input or chaining different actions based on what the agent finds, build an agent. We pick the right category in week one — not because the client asked for an agent, but because the work needs one.
Production Patterns
3 AI Agent Patterns We Ship Most
These three patterns cover the vast majority of production AI agent work for B2B SaaS. We have shipped variants of each. The tooling and architecture per pattern is below.
Customer Support Agent
Deflects 40-65% of tier-1 tickets autonomously
An agent that lives inside your help center and your in-app chat. It reads your knowledge base, queries your customer records, checks subscription and billing state, and resolves issues end-to-end. Issues refunds within policy, updates account settings, escalates anything outside its confidence threshold to a human with full context.
Tools we wire in:
- • Knowledge base retrieval (RAG)
- • CRM read + write (HubSpot, Pipedrive)
- • Stripe customer + subscription actions
- • Internal ticket creation + escalation
Stack:
- • OpenAI GPT-4o + Anthropic Claude failover
- • pgvector for knowledge base RAG
- • LangGraph for multi-step orchestration
- • Structured outputs for action validation
Operations & Engineering Agent
Monitors, diagnoses, and remediates routine ops events
An agent that watches your observability stack and acts on routine incidents. Reads logs and traces, correlates errors, identifies likely root cause, runs safe remediation steps (restart a worker, scale a service, flush a stuck queue), and pages a human only when the issue is outside its playbook.
Tools we wire in:
- • Sentry, Datadog, CloudWatch log retrieval
- • AWS / Vercel control plane (read + scoped write)
- • GitHub PR creation for code-level fixes
- • PagerDuty / Slack for human escalation
Stack:
- • Claude 3.5 Sonnet for long-context log analysis
- • Function calling for safe-list infrastructure actions
- • Audit log on every action taken
- • Hard guardrails — no destructive actions without approval
Research & Analysis Agent
Synthesizes findings across unstructured sources
An agent that reads across long-form sources — meeting notes, customer feedback, support transcripts, market reports — and produces structured findings on demand. Cites sources. Flags low-confidence claims. Updates as new data arrives.
Tools we wire in:
- • Document ingestion + embedding pipeline
- • Vector search across your sources
- • Web search where allowed
- • Output to Notion / Linear / your dashboard
Stack:
- • Claude Opus for synthesis (long context)
- • GPT-4o for extraction
- • Citation enforcement layer
- • Confidence scoring on every claim
The Agent Stack We Build On
Modern AI agents need more than a single LLM call. Production agents need orchestration, tool definition, observability, cost control, and safety. Here is the stack we use.
LLM Providers
- OpenAI GPT-4o + GPT-4
- Anthropic Claude 3.5 Sonnet + Opus
- Gemini 2.0 (cost-tier)
- Multi-provider failover layer
Orchestration
- LangGraph for multi-step flows
- OpenAI Assistants API where it fits
- Custom state machines for known shapes
- Anthropic MCP for tool integration
Tool Integration
- Function calling with JSON schema validation
- API wrappers with auth + rate limiting
- Database read + write with row-level security
- Webhook + event handlers
Memory + State
- Conversation memory in Redis
- Long-term memory in pgvector
- Per-tenant isolation
- Audit log of every decision
Observability
- LangSmith / Helicone for traces
- Per-call cost tracking
- Latency + token dashboards
- Anomaly alerts on spend
Safety + Guardrails
- Structured output validation
- Action allow-lists per agent
- Human approval for high-impact actions
- Hallucination detection on critical outputs
Why Founders Pick Us for AI Agent Work
We say no when the answer is workflow, not agent.
Most AI agent quotes you will get are for tasks that should be deterministic workflows. We tell you when that is the case — even if it costs us the bigger project.
Cost projection before code.
Every engagement starts with a cost model. Per-call cost. Monthly projection at three growth scenarios. You see what the agent will cost at 1K, 10K, and 100K users before week two starts.
Built into your existing SaaS, not bolted on.
We integrate with your auth, your data, your billing — not a separate app users have to learn. The agent feels native to your product because it is native to your product.
Hard guardrails, not optional.
Every production agent we ship has structured-output validation, action allow-lists, audit logs, and human-in-the-loop for anything high-impact. Safety is part of the architecture, not a post-launch patch.
AI Agents for SaaS — Frequently Asked Questions
Scope an AI Agent for Your SaaS
Tell us what work your team does manually that you wish ran on autopilot. We will tell you whether an agent, a workflow, or something simpler is the right answer — and the realistic cost to build it.
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