AI · Agents & chatbots
AI Agents & Chatbots for Business
Customer-facing and internal AI agents that answer from your own data, act through your systems, and know when to hand over to a person. Built with guardrails and measured before launch.
What AI agents and chatbots does Co-Mate build?
Co-Mate builds AI agents and chatbots that answer from your own documents and data, work on WhatsApp, web chat and internal tools, and take actions through your systems' APIs. Each has guardrails, an evaluation set of real questions, audit logs and a clean hand-off to people, so it is safe to put in front of customers.
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AI agents and chatbots are part of Co-Mate's digital transformation services. We build them for production: grounded, evaluated, permissioned and monitored, not as demos.
Who this is for
Who are AI agents for?
Companies where a large share of conversations or internal requests are repetitive, and the answers already exist in documents, databases or systems.
Typical buyers
- Customer service and sales heads handling high message volumes
- COOs looking to scale support without scaling headcount at the same rate
- CTOs and product leaders adding AI to customer-facing products
- HR, IT and finance teams fielding the same internal questions every day
Signs you need it
- Your team answers the same questions about products, orders, policies or status every day.
- Customers wait hours for replies outside office hours.
- Staff search shared drives and manuals to answer colleagues' questions.
- Simple actions such as rescheduling or order lookups need a person to log into several systems.
- You tried a generic chatbot and it gave confident, wrong answers.
What we build
What kinds of AI agents do we build?
Agents differ by who uses them and what they are allowed to do. We match the design to the risk.
| Agent type | What it does |
|---|---|
| Customer-facing chat agent | Answers product, order, policy and booking questions on your website or app, from live data and approved content. |
| WhatsApp agent | Handles enquiries on the official WhatsApp Business Platform, with team inbox hand-off. See WhatsApp business automation. |
| Document Q&A (RAG) | Answers staff questions from manuals, contracts, SOPs and policies, citing the exact source passage. |
| Tool-calling agent | Takes actions through your APIs: creates tickets, updates CRM records, checks stock, books slots, within strict permissions. |
| Internal operations agent | Prepares reports, drafts replies, triages requests and routes work to the right team. |
| Voice and email agents | Classifies and drafts replies to incoming email, or answers routine calls, with human approval where needed. |
How do you make an AI agent safe to put in front of customers?
By narrowing what it can say and do, grounding every answer in approved data, measuring it before launch and keeping a person in the loop for anything uncertain.
- Scope: the agent handles a defined list of topics and politely declines everything else.
- Grounding: answers come from retrieved documents and live data, never from the model's general memory about your business.
- Permissions: each tool the agent can call has its own limits; refunds, cancellations or record changes can require confirmation or approval.
- Evaluation: a set of real questions with expected answers is scored on every release, so regressions are caught before customers see them.
- Hand-off: the agent escalates on low confidence, sensitive topics or customer request, with the conversation history attached.
- Audit: every conversation, retrieved source and action is logged for review.
Which use cases pay off first?
The best first use cases are high-volume, answerable from data you already have, and low-risk if a reply needs correcting. Typical examples are order and delivery status, product availability and specifications, appointment booking, internal policy and HR questions, and first-line IT support.
Use cases with ambiguous answers or high financial impact come later, once the agent has proven itself and approvals are in place. Our guide on AI agents for business ranks common use cases by payoff and risk.
How does an engagement work?
Five phases, each with a clear output you can review before the next one starts.
- 1
Discovery
We pick one high-volume use case, collect real questions and expected answers, and agree what the agent may and may not do.
- 2
Architecture & scope
You get the agent design: data sources, retrieval approach, tools and permissions, model choice, guardrails and the evaluation set.
- 3
Build in milestones
The agent is built and scored against the evaluation set at every milestone, then piloted with internal users.
- 4
Launch
Rollout to customers or staff with monitoring, human hand-off and a feedback loop from real conversations.
- 5
Support & scale
Regular evaluation runs, new tools and data sources, model updates and cost tuning as usage grows.
Which engagement model fits?
There are three ways to work with Co-Mate; the right one depends on how settled your scope is and how long the system will keep evolving.
| Model | Best for | How it works |
|---|---|---|
| Fixed-scope project | A defined first version or a specific integration with clear requirements. | We agree modules, milestones and acceptance criteria up front, then deliver against them. Changes are scoped and agreed before work starts. |
| Dedicated engineering team | Larger programmes, evolving roadmaps or several systems built in parallel. | Senior engineers work as an extension of your team on your priorities, with sprint planning, demos and shared tooling. |
| Ongoing retainer / support | Systems already live that need maintenance, monitoring, hosting support and steady improvements. | A reserved block of engineering time each month, with agreed response times for issues and a backlog of improvements. |
A first agent fits well as a fixed-scope project with clear accuracy targets. Because agents improve with real conversations, most clients then keep a retainer for evaluation, tuning and new capabilities; AI-first products often need a dedicated team.
What can the agent connect to?
An agent is only as useful as the systems it can read and act on. We connect it through official APIs and, where useful, expose your system through an MCP server, as described in AI layer for existing software.
- WhatsApp Business Platform
- Website and in-app chat
- Email inboxes
- CRM (custom, Zoho, HubSpot, Salesforce)
- ERP (Odoo, SAP B1, ERPNext)
- Help desk and ticketing
- SharePoint and Google Drive
- Databases (PostgreSQL, SQL Server, MySQL)
- OpenAI models
- Anthropic models
- Open models (self-hosted)
- n8n workflows
Product names are used only to describe compatibility. They belong to their owners; Co-Mate is not affiliated with or endorsed by them.
Tech & infrastructure
Grounded answers
Retrieval over a vector index of your content, with source citations and refusal when the answer is not found.
Guardrails
Scope limits, input and output checks, role-based data access and confirmation before high-impact actions.
Evaluation
A test set of real questions scored on every change, plus monitoring of live conversations.
Deployment & cost
Cloud APIs or self-hosted models, with caching, model routing and usage limits to keep running costs predictable.
Where do we work?
Anywhere. Co-Mate is a remote-first engineering studio in Kerala, India, delivering for businesses worldwide.
GCC
Our working day covers the whole GCC business morning and early afternoon.
Europe & UK
Strong overlap with European and UK working hours for calls, reviews and releases.
US & Americas
Scheduled overlap with US mornings for workshops, demos and decision meetings.
India, APAC & Australia
Same or near time zone, so day-to-day collaboration is simple.
Discovery workshops, demos and training run online. You talk directly to the senior engineers who design and build your system, not to an account manager relaying messages.