AI Agents for Task-Scoped Automation
An AI agent is an autonomous worker shaped around a specific, bounded task — extracting invoice data, running competitive research, processing a backlog, pulling a weekly report. It's different from an AI employee, which fills an ongoing role. Agents fire, work, and stop. We build them custom, wire them to your systems, and put them on whatever trigger fits the job.
What we build agents to do
Every agent is custom — but these are the patterns we build most often. Yours probably maps to one or two of them.
Browser Automation
Operates legacy portals, SaaS apps without APIs, and web interfaces the way a human does. Logs in, clicks, fills forms, downloads, uploads. Resilient to minor UI changes.
Example: Pulling weekly reports from an insurance carrier portal and posting them to Slack
Document Extraction
Reads PDFs, scanned invoices, contracts, and forms. Extracts structured data, validates it, and writes it into your systems. Handles variable formats.
Example: Processing 500 supplier invoices a week into your ERP with fields validated
Research & Intelligence
Scans the web, competitors, filings, and news for specific signals. Summarizes, flags changes, and compiles briefs on a schedule or trigger.
Example: Monitoring 20 competitors' pricing pages and alerting when any changes
Email Triage
Classifies incoming email by intent, extracts key data, routes to the right team, and drafts templated responses. Handles shared inboxes at scale.
Example: A 200-emails-a-day shared inbox sorted, tagged, and partially-answered within seconds
Data Migration
Moves records between systems during platform migrations, consolidations, or one-off imports. Maps schemas, cleans data, handles edge cases.
Example: Migrating 100K customer records from a legacy CRM into HubSpot with deduplication
Lead Enrichment
Takes a lead name or company and enriches with firmographics, contact data, buying signals, and tech stack. Populates your CRM on intake.
Example: Every inbound lead auto-enriched with 15+ fields before it hits a rep's queue
Report Generation
Pulls data from multiple sources on a schedule, compiles into formatted reports, and distributes. Handles exception alerts when KPIs breach.
Example: Monday-morning exec dashboards assembled from 6 tools, automatically
Custom Task Agent
Don't see your task here? We build bespoke agents for specific business problems — from pricing updates to inventory checks to contract review.
Example: A custom agent that does exactly the thing eating your team's Thursday afternoons
Why AI agents outperform traditional automation
Robotic Process Automation (RPA) was built for a world where inputs never varied and UIs never changed. Most businesses don't live in that world. AI agents handle what RPA can't.
| Dimension | Traditional RPA | Brainova AI Agent |
|---|---|---|
| Behavior | Follows a rigid script — same steps, every run | Reasons about what to do — handles new inputs, variable formats, ambiguity |
| Resilience | Breaks when the UI changes or an input looks different | Adapts to minor changes — tells you when it can't instead of failing silently |
| Scope | Narrow, deterministic: if-this-then-that | Broader: plans multi-step work, uses judgment within guardrails |
| Setup cost | High upfront mapping of every click and field | Described in plain language; the model figures out the how |
| Maintenance | Fragile — scripts need constant babysitting | Self-adjusting within limits; flags exceptions for human review |
Three ways an agent gets its work
Most agents support multiple trigger types — pick the ones that fit the work.
Scheduled
Runs on a recurring schedule — hourly, daily, weekly, monthly. Right for reports, monitoring, refreshes, and recurring data tasks.
Event-triggered
Fires when something happens — a form is submitted, an email arrives, a record is updated, a webhook is received. Right for real-time workflows.
On-demand
Invoked via API, Slack command, internal dashboard, or human request. Right for ad-hoc tasks and augmenting your team's work.
Frequently Asked Questions
About the Service
An AI agent is task-shaped — it completes a specific bounded task (extract data from this PDF, monitor this competitor, run this weekly report) and stops. An AI employee is role-shaped — it fills an ongoing role with multiple responsibilities and decision authority. Rule of thumb: if the work has a clear start and stop, it's an agent job. If it's a persistent role with varied inputs and judgment, it's an employee job. Most businesses use both.
RPA follows rigid scripts — if the UI or input format changes, the bot breaks. AI agents reason about what they're doing, adapt to variation, and know when to stop and ask. They also deploy much faster: RPA requires mapping every click and field in advance; AI agents are described in plain language. RPA still wins for locked-down, never-changes workflows. AI agents win for anything involving judgment, variable inputs, or evolving systems.
Yes — this is actually one of the most common use cases. Our browser-automation agents operate web interfaces the way a human would: they log in, navigate, fill forms, click buttons, and extract information. This is how we integrate with legacy portals, industry-specific SaaS, and internal systems that pre-date modern APIs.
Three common patterns: scheduled (runs on a cron — daily, hourly, weekly), event-triggered (fires on a webhook, inbound email, record change, or form submission), or on-demand (invoked via API, Slack command, or internal dashboard). Most agents support multiple trigger types — e.g., a lead enrichment agent that runs automatically on new leads and can also be invoked manually from your CRM.
Getting Started
Simple single-task agents start around $3,000–$5,000. Multi-step agents with integrations typically run $5,000–$15,000. Complex agents (e.g., multi-system data migrations, high-volume document processing) can exceed $20,000. Ongoing Managed support starts at $500/month. Every project includes a fixed-price quote after the Assessment phase.
Reliability depends on design, not hope. We build every agent with validation checks, defined fallback behavior, and explicit exception paths — if the agent is unsure, it escalates with full context instead of guessing. We also instrument everything: every decision is logged, every exception is tracked, and you have monitoring dashboards showing what ran, what succeeded, and what needs review.
Yes — and this is where real leverage comes from. A lead-enrichment agent might feed into an outbound SDR employee. A document-extraction agent might feed into an accounting workflow. We design with composition in mind and reuse infrastructure across agents so the second, third, and fourth deploy faster than the first.
Simple single-task agents: 1–3 weeks. Multi-step agents with integrations: 3–6 weeks. Complex agents with custom model work or high reliability requirements: 6–10 weeks. You get a firm timeline during the Strategy & Architecture phase — before any build work begins.
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