Custom AI Agent Development
Off-the-shelf copilots don't know your business. We build the one that does.
Custom AI agent development services for unique, high-value workflows.
Production-ready bespoke AI agents for your specific workflow — not generic copilots, not templated chatbots, not productized back-office bots. You own the code, the prompts, the data, the IP.
How a custom agent is built
Reasoning layer
LLM (Claude, GPT, Gemini, open model)
Tools & integrations
APIs, CRM, ERP, databases, your systems
Memory & context
Short-term + long-term · Permission-aware
Agent orchestration
LangGraph · CrewAI · OpenAI Agents SDK · MCP
You own all code, prompts, and IP
Feasibility Review
From $2K
This page vs Agentic Process Automation
This page is for bespoke, unique-workflow AI agents — the ones you can't buy off the shelf. For productized back-office automation (invoice-to-pay, ticket triage, reconciliation), see Agentic Process Automation— that's where our 4-week Agent Sprint lives.
Use this page when your workflow is deeper than a template and business-critical enough that you want your own IP, not a vendor's black box.
This page is for you if the workflow is:
- Unique to your business, domain, or data model
- Deeper than a template (custom logic, proprietary reasoning, internal jargon)
- Business-critical enough that you want your own IP, not a vendor's black box
Agent Feasibility Review — 1 week
Not ready to commit to a custom build? Start here. One week. We scope your workflow, interview your team, prototype the core agent logic, and deliver a feasibility report — technical architecture, cost/timeline estimate, risk flags, and a go/no-go recommendation. If we build together, the fee is credited toward the build.
What you get in 1 week
- Workflow deep-dive + team interviews
- Prototype of the core agent loop
- Architecture, cost, and timeline estimate
- Risk flags + go/no-go recommendation
The problem
Off-the-shelf copilots are too generic for your workflow. Zapier and Make hit ceilings at real complexity. Your internal team can prototype — but productionizing is another story: auth, observability, evals, guardrails, error handling, model deprecation, scaling.
Meanwhile, your highest-value workflows are still manual:
- SDRs researching accounts and drafting outreach
- AP clerks matching invoices across systems
- Support engineers triaging and diagnosing Tier-2 tickets
- Analysts pulling data across 6 tools to answer one question
- Recruiters screening and scheduling 50 candidates a week
These are agent-shaped problems. You just need someone to build them right.
Design beats guesswork
Hallucination is a design problem, not an LLM problem.
We use evals, deterministic tool calls, and guardrails so the agent knows when to act, when to ask for approval, and when to escalate.
AI agent vs chatbot — what's actually different
The short version: chatbots converse. Agents act.
| Chatbot | Custom AI agent | |
|---|---|---|
| What it does | Converses | Acts — calls tools, reads/writes systems, completes work |
| Decision-making | Scripted / retrieval | LLM reasoning with guardrails |
| Tool use | Rare | Core — CRM, calendar, database, email, API |
| Output quality gate | Sounds right? | Evaluated against success criteria + eval harness |
| Example | “Where’s my order?” → shows link | “Where’s my order?” → checks order system, triggers refund if appropriate, writes back to CRM |
| Best for | FAQ, routing, light support | Work completion, decision-support, process automation |
| Deployment complexity | Low | Medium–High (worth it for high-value workflows) |
What we build
Bespoke agents for sales, support, finance, research, and internal teams — tuned to your data and systems.
Sales & revenue agents
- SDR research agent: account intel, firmographic data, personalized outreach drafts
- Lead qualification and routing agent
- Quote and proposal generation agent
- CRM hygiene agent (dedup, enrichment, activity logging)
Support & CX agents
- L2 support agent with tool access (order lookup, refund initiation, account updates)
- Ticket triage + resolution suggestions beyond Tier-1 templates
- Customer health monitoring with proactive alerts
- Survey and feedback analysis agent
Finance & operations agents
- Specialized AR/AP agents with domain-specific logic
- Contract negotiation assistants
- Treasury and cash-flow agents
- Audit-ready reconciliation for niche ledgers
Research & analyst agents
- RFP response drafting agent
- Competitive intel monitoring with summarization
- Market research synthesis
- Investment memo drafting
People / recruiting agents
- Candidate screening with structured scoring
- Interview scheduling across time zones
- Onboarding task orchestration
Internal-tool & analyst agents
- “Ask your data” analyst agent over your warehouse
- Internal Q&A assistant over SOPs and policies (for cross-document, see Knowledge Intelligence)
- Meeting summarizer + action-item tracker
For productized back-office automation (invoice-to-pay, IT ticket triage, vendor onboarding) — see Agentic Process Automation.
Our engineering approach — the ConverseAI Agent Reliability Stack
Stack picked per project:
Frameworks
LangGraph, CrewAI, OpenAI Agents SDK, Claude Agent SDK
Model providers
Anthropic, OpenAI, Google, Mistral, open models (Llama, Qwen)
Vector
Pinecone, Weaviate, pgvector, Turbopuffer
Observability
LangSmith, Langfuse, Arize, Helicone
Deploy
AWS, Azure, GCP, on-prem — your cloud, your control
What every agent ships with
01
Eval harness
Test suite covering success cases, edge cases, adversarial inputs. We don’t deploy what we can’t measure.
02
Observability dashboard
What the agent did, why, token spend, error rates.
03
Guardrails
Deterministic tool calls for irreversible actions. HITL on high-risk steps.
04
Runbook
What to do when things go wrong. Who to call. How to intervene.
05
Handoff docs
Architecture, prompt library, config, deployment — everything your team needs to own it.
How it works
Week 0 — Scoping call (free).
Workflow, success metrics, constraints, stack.
Week 1 — Agent Feasibility Review.
Architecture, cost/timeline estimate, go/no-go.
Weeks 2–7 — Build.
Working agent by week 4. Weekly demos. Iterate.
Week 8 — Deploy.
Shadow → HITL → autonomous. Monitored go-live.
Ongoing (optional).
Retainer for tuning, coverage expansion, model updates.
Why ConverseAI
Own your agent, own your data. No per-conversation metering, no lock-in, and you can move to open models the day provider pricing shifts.
Product-tested team. We run agents at scale in our own SaaS — not a pure services shop learning on client bills.
Full-stack in one team. Prompt engineering, backend, integration, eval — no handoffs.
No framework lock-in. Claude, GPT, Gemini, Llama. LangGraph, CrewAI, OpenAI Agents SDK. We pick per project.
India delivery economics. 50–60% the cost of US-only firms, same engineering stack.
Outcomes you can expect
- Production agent live in 4–8 weeks
- 60–80% of the target workflow automated end-to-end
- 40–70% reduction in cost-per-ticket / case / lead
- One agent equivalent to 3–8 FTEs on repetitive work
- Move models without a rebuild as provider pricing shifts
Prefer productized back-office automation? See Agentic Process Automation.
How we compare
Production-grade AI agents without the enterprise price tag or freelance risk.
| ConverseAI | Enterprise AI vendor (Accenture, Deloitte) | Freelance / offshore dev | |
|---|---|---|---|
| Timeline | 4–8 weeks to first production agent | 12–24 weeks | Unpredictable |
| Team | Engineers who ship AI in production | Partners + junior MBAs | Single dev, no AI ops experience |
| Deliverable | Working agent + eval harness + runbook | Architecture doc + recommendations | Code only |
| Eval & testing | Full test suite, every agent | Varies | Manual only |
| Production support | Observability dashboard + retainer available | Separate support contract | Best effort |
| Best for | Mid-market with unique workflows needing production-grade agents | Fortune 500 transformation programs | Proof-of-concept only |
FAQs
How much does it cost to build a custom AI agent?
We start with a 1-week Agent Feasibility Review that scopes your workflow, prototypes the core logic, and delivers a go/no-go recommendation. All builds are fixed-fee and fixed-timeline — no T&M.
How long does AI agent development take?
4 weeks for the productized Agent Sprint. 6–10 weeks for complex bespoke builds with deep integrations and high reliability requirements.
What's the difference between a chatbot and an AI agent?
Chatbots converse. Agents act — they call tools, read/write to systems, make decisions, complete work. See the comparison table above.
Can AI agents integrate with our existing tools?
Yes — CRMs, ERPs, helpdesks, databases, internal APIs, SaaS tools with webhooks or APIs. If it has an interface, we can connect. See AI Integration Services for integration-only engagements.
Which model / framework do you use — are we locked in?
No lock-in. We pick the best fit for your use case. Your agent is portable across providers; we document the swap path.
Who owns the code and the IP?
You. Full source, prompts, configs, and docs are yours on delivery. We keep nothing proprietary to us.
How do you handle hallucinations and guardrails?
Deterministic tool calls for irreversible actions, HITL approval gates, strict output schemas, retry logic, eval harnesses. Hallucination is a design problem, not an LLM problem.
Can it work with our on-prem / VPC / private LLM?
Yes. AWS Bedrock, Azure OpenAI, GCP Vertex, VPC deployments, and open-source models (Llama, Qwen, Mistral) on your infrastructure.
What happens when the underlying model changes or deprecates?
We build with abstraction layers so you can swap models. Our retainer clients get model-migration support included.
Custom AI Agent Development vs Agentic Process Automation — which do I need?
Use Agentic Process Automation for productized back-office workflows (invoice-to-pay, ticket triage, reconciliation). Use Custom AI Agent Development for unique workflows not on our productized list — bespoke logic, proprietary data, domain-specific reasoning. Not sure? Start with a Feasibility Review.
Need integration-only help? See AI Integration Services.