# Meet your digital workforce. Ten operational AI agents — what each one solves, what it actually does, what it connects to, and where a person stays in the loop. ## Sales Agent Qualifies leads, answers product questions and books meetings. - **The problem** — Inbound leads wait hours or days for a first response, and by the time someone replies the buyer has moved on. - **What it does** — Responds to inbound enquiries; Qualifies against your criteria; Answers product and pricing questions from approved material; Books meetings against live calendars; Writes the record and notes to the CRM; Routes to the right owner - **What it connects to** — HubSpot, Salesforce, Google Calendar, Outlook, Slack, Web forms - **The outcome** — Every lead gets an immediate, informed first response, and reps spend their time on qualified conversations. How we build this: [Read more](https://www.bralakai.com/ai-agent-development). ## Customer Support Agent Handles repetitive support conversations and escalates the rest with context. - **The problem** — A large share of tickets are the same handful of questions, and they sit in the same queue as the cases that genuinely need a specialist. - **What it does** — Answers from documentation and policy; Retrieves account and order history; Performs account actions within policy; Escalates with a written summary; Tags and routes; Identifies documentation gaps - **What it connects to** — Zendesk, Intercom, Freshdesk, Salesforce Service Cloud, Slack, Email - **The outcome** — Routine cases resolve without a queue; specialists receive pre-researched tickets. How we build this: [Read more](https://www.bralakai.com/ai-agent-development). ## Voice Agent Answers and places calls, completing real tasks in conversation. - **The problem** — Calls arrive when nobody is free, and the work stops while someone answers the phone. - **What it does** — Answers inbound calls; Places outbound calls; Verifies callers; Retrieves live account data; Books and reschedules; Updates the CRM; Transfers with a spoken summary; Files a transcript and summary - **What it connects to** — Twilio, SIP, Salesforce, HubSpot, Scheduling platforms, Calendars - **The outcome** — Calls are handled immediately, and the ones that need a person arrive with context. How we build this: [Read more](https://www.bralakai.com/ai-voice-agents). ## Research Agent Gathers, analyses and summarises information from defined sources. - **The problem** — Preparation work is valuable and reliably the first thing dropped when the week gets busy. - **What it does** — Gathers from approved sources; Extracts what is relevant to the brief; Compares against your criteria; Produces a cited summary; Flags what it could not verify - **What it connects to** — Internal knowledge bases, Document stores, Approved external sources, Slack, Notion - **The outcome** — Preparation happens consistently instead of when there is time for it. How we build this: [Read more](https://www.bralakai.com/multi-agent-systems). ## Knowledge Agent Answers questions from organisational documentation, with citations. - **The problem** — The answer exists — in a document, a thread or one person’s memory — and finding it takes longer than the task it unblocks. - **What it does** — Answers from internal documentation; Applies the asker’s permissions; Cites sources; Says so when the answer is not documented; Logs unanswered questions - **What it connects to** — SharePoint, Google Drive, Confluence, Notion, Internal wikis, Slack, Teams - **The outcome** — Institutional knowledge becomes reachable, and the gaps become visible. How we build this: [Read more](https://www.bralakai.com/rag-development). ## Document Agent Extracts, validates and processes business documents. - **The problem** — Documents arrive in every format and someone retypes them into a system. - **What it does** — Classifies document type; Extracts structured fields; Validates against systems of record; Flags discrepancies with both sources; Files and posts; Requests what is missing - **What it connects to** — Email, SFTP, S3, SharePoint, Accounting systems, ERP, Document stores - **The outcome** — Intake stops being manual, and exceptions surface with a diagnosis attached. How we build this: [Read more](https://www.bralakai.com/ai-automation). ## Operations Agent Monitors workflows and initiates action when something needs attention. - **The problem** — Problems are found when someone happens to look, which is usually after they matter. - **What it does** — Monitors defined signals; Diagnoses against known patterns; Applies documented remediations; Raises incidents with diagnosis; Notifies the right owner; Tracks to resolution - **What it connects to** — Monitoring platforms, Ticketing, Slack, Teams, Internal APIs, Databases - **The outcome** — Known issues are handled as they occur rather than discovered later. How we build this: [Read more](https://www.bralakai.com/ai-automation). ## Recruitment Agent Supports candidate qualification, scheduling and coordination. - **The problem** — Good candidates are lost to slow scheduling and unanswered follow-ups. - **What it does** — Screens applications against defined requirements; Answers candidate questions; Coordinates interview scheduling; Sends updates at every stage; Maintains the ATS record - **What it connects to** — ATS platforms, Calendars, Email, Slack - **The outcome** — Candidates get prompt, consistent communication and hiring managers see prepared shortlists. How we build this: [Read more](https://www.bralakai.com/ai-automation). ## Finance Agent Handles structured financial workflows and document intelligence. - **The problem** — Finance operations run on repetitive matching, chasing and reconciliation work. - **What it does** — Processes invoices; Matches against purchase orders; Validates tolerances; Flags discrepancies; Chases approvals; Prepares reconciliation summaries - **What it connects to** — Xero, QuickBooks, NetSuite, SAP, Banking APIs, Email - **The outcome** — Finance reviews exceptions rather than performing matching by hand. *Financial actions default to human approval.* How we build this: [Read more](https://www.bralakai.com/ai-automation). ## Custom Agent Built around a workflow specific to your business. - **The problem** — The workflow that costs you most is usually the one no product was built for. - **What it does** — Defined by the workflow — scoped during discovery against your systems, your rules and your approval requirements - **What it connects to** — Whatever the workflow touches - **The outcome** — The process that was too specific to buy software for gets automated anyway. How we build this: [Read more](https://www.bralakai.com/ai-consulting). ## Related - [AI Agents vs Chatbots (article)](https://www.bralakai.com/insights/ai-agents-vs-chatbots) — The comparison is usually framed as a contest of conversational quality. It is not. The line between the two is consequence — whether the system can change anything — and everything that matters about building either one follows from which side of it you are on. ## Your Next Intelligent System Starts Here. Tell us what you’re trying to improve, automate or build. We’ll help you identify the right AI strategy and engineering path. - [Book an AI Strategy Call](https://www.bralakai.com/contact) - [Start a Project](https://www.bralakai.com/contact) --- *Bralak AI — Building Intelligent Solutions · Automating the Future.* Bralak AI Pvt. Ltd. — Noida, UP, India. - Canonical page: https://www.bralakai.com/agents - Agent index: https://www.bralakai.com/llms.txt · full text: https://www.bralakai.com/llms-full.txt - Contact: info@bralakai.com · https://www.bralakai.com/contact