Custom AI agent development for business workflows
Scoped AI agents that call your tools, follow role rules, and escalate to humans—built for SMBs and product teams in India, not vapour demos.
Businesses hire partners for AI agents when staff repeat the same lookups, ticket replies, booking steps, or document handoffs. The buyer is usually an operations lead, founder, or IT owner who wants hours back—not a research paper.
Trevyk builds custom agents that combine a language model with approved tools (APIs, search, forms, messaging). We start with one workflow: intake, retrieval or classification, proposed action, human review where risk is high, then an audit log. Multi-agent orchestration comes only after the first path is stable.
Cost depends on integrations, languages, traffic, evaluation depth, and whether write actions are allowed. We quote from your scope. You keep the source for custom work. We do not invent school counts or claim an agent is live inside Kiduart ERP unless that product surface has shipped.
Agent types we actually scope
Support triage agents classify and draft replies, then hand off to a person when confidence is low or policy requires it.
Operations agents look up order, ticket, or CRM state through your APIs and prepare the next step—never inventing a record that is not returned by a tool.
Document and intake agents extract fields, validate them against rules, and open a case for staff confirmation on sensitive writes.
Internal copilots help employees search approved knowledge; they are not unsupervised spenders, buyers, or exam graders.
Workflow and architecture
A practical agent loop is: understand the request, decide which tool (if any) to call, execute with structured inputs, check the result, answer or act, and log the path. Predictable steps belong in deterministic code; the model should not reinvent them every time.
Enterprise guidance from providers such as Google Cloud emphasises loading only task-specific tools rather than stuffing every API into context, wrapping external APIs so the model sees only relevant fields, and governing tool calls with identity, policy, and audit.
We write role instructions, tool schemas, refusal rules, and iteration limits before the first demo. That documentation becomes part of the handoff.
Tool calling, integrations, and memory
Approved integrations are listed in the scope: CRM, helpdesk, ERP read endpoints, WhatsApp or web widgets, calendars, and internal search. Write actions stay behind permissions and usually behind confirmation.
Short-term conversation memory is fine for a session. Long-term memory of customer facts belongs in your systems of record—not in an opaque model cache—so access controls stay enforceable.
When several tools must run together, we prefer one composed tool or a callback chain so behaviour stays deterministic instead of hoping the model invents the sequence.
Human-in-the-loop, reliability, and security
Human approval sits on money moves, identity changes, exam or fee updates, legal language, and any action your policy marks as irreversible.
Reliability work includes an evaluation set of real questions, simulated tool failures, grounding checks where RAG is involved, and traces you can review after an incident.
Security basics: least-privilege service accounts, secrets outside the prompt, PII redaction where needed, rate limits, and a clear kill switch. Failure handling returns a safe message and opens a human queue—never a silent wrong write.
Implementation phases and what you receive
Discovery maps the workflow, data sources, roles, and decisions a human must keep. Prototype proves the path on a narrow corpus or sandbox API. Hardening adds evals, logging, and production credentials. Operate covers monitoring, cost alerts, and a change process for prompts and tools.
Typical deliverables: scoped agent service or module, tool wrappers, prompt and policy notes, eval checklist, runbook, and source ownership for custom code.
Voice agents are optional and only proposed when the channel and latency requirements are real—not as a default upsell.
What you leave with
- One primary workflow before multi-agent theatre
- Precise tool definitions and least-privilege access
- Human escalation and approval checkpoints
- Eval set, traces, and cost monitoring notes
Questions buyers ask
What is a custom AI agent in this context?
Software that uses a language model plus tools (APIs, search, forms) to complete a defined business workflow, with logging and a path to a human when confidence is low or policy requires review.
Do you only use Gemini?
The public Trevyk site assistant uses Gemini today. Custom projects can integrate other model providers when the engagement requires it; we choose based on cost, privacy, latency, and quality for the workflow.
Can an agent update our ERP or CRM?
Yes when you authorize scoped API access and accept audit logging. Sensitive writes usually require confirmation or human review.
How do you measure success?
Against an agreed evaluation set: task completion, safe refusals, grounding where documents are required, escalation rate, latency, and token cost—not vanity chat volume.