Artificial Intelligence and Data
AI agents
A real agent is more than a chat that sounds smart: it pursues a goal, pulls information from the right sources (approved internal docs, CRM, permitted spreadsheets), chooses the next step, and knows when to stop and hand off to a person. Viscale designs this like any critical software—clear boundaries for solo actions, logs of what was read and decided, edge-case tests with real scenarios, and predictable usage cost—integrating the model APIs you already pay for or hosting the flow on your preferred infrastructure.
We always start on the whiteboard: who is the user, which repetitive task hurts today, and what must never leave the script (for example, sending external email without review). Only then do we pick tools—light orchestration, semantic search over approved PDFs, or integration with the system where the order is born. The agent gets explicit tools (look up order, open ticket) instead of inventing a risky shortcut.
Examples we build
Tier-1 support assistant
Reads an approved knowledge base, drafts replies, escalates with history if the customer pushes back.
Sales proposal pre-check
Crosses client PDFs with an internal checklist before the rep calls.
Resume triage with fixed rules
Summarizes and scores against the published role; HR only sees what cleared the agreed bar.
Internal onboarding buddy
Answers “how do I do X” from a versioned handbook plus directory links.
Bid response drafting helper
Pulls mandatory items from the tender and maps to catalog; legal reviews before submit.
SLA monitor in plain language
Queries the ticket queue and summarizes what missed deadlines and why—for the daily stand-up.
Store manager assistant
“How much did we sell of X this week?” with numbers from BI or ERP, not model guesses.
Meeting minutes prep
Transcript plus minutes template; a participant edits and publishes—nothing goes out without a final click.
Light compliance checker
Compares campaign copy against banned terms and a short internal policy summary.
Internal code copilot
Only over an authorized repo—no leaking secrets outward; hints aligned to team standards.
Security and privacy are part of the design: what may go to the model, what stays in your database, and how to mask sensitive data in logs. We measure quality with a test set you sign off—not just “looks right.” We leave a simple dashboard or export: runs, failures, rough cost per batch—so the business can follow along without SQL.
When the process changes (new contract, new CRM field), the playbook and tests update with versioning. If you switch model providers later, the core (rules, sources, APIs) stays coherent—we swap the engine, not your business logic.
Portfolio of AI agents
Deliverables
Agent in production or staging
As agreed, with URL or channel integration.
Flow specification
Diagram and prose explaining behavior for non-technical readers.
Tool catalog
Each action the agent can take and its prerequisites.
Documented test set
Expected input, acceptable output, and escalation rules.
Data and retention policy
What enters the model, for how long, and where logs live.
Operational runbook
Pause, reprocess, and incident contacts.
Usage dashboard or export
Volume, errors, and order-of-magnitude cost.
Secrets in a vault
Integration without keys in source code.
Versioned scripts or repo
So internal teams can evolve with traceability.
Handoff session
Walkthrough with whoever maintains the agent.
Prompt review cadence plan
Suggested rhythm when models or the business change.
Next-agent ideas
Short backlog from what we learned together.
Execution methodology
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Map the task and risks
What to automate, what needs a human, and what is off limits.
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Source inventory
Documents, APIs, permissions—the agent only sees what is allowed.
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Playbook and tools
Steps, system calls, and default messages when unsure.
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Prototype with synthetic data
End-to-end flow before touching real records.
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Guardrails and policies
Output filters, context limits, and blocked topics.
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Edge-case tests
A list the business validates: pass, reject, escalate.
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Integration and queues
Resilience, timeouts, and retries when external APIs wobble.
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Observability
Step logs, estimated cost, and alerts for mass failures.
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Controlled pilot
Small group or limited hours until stable.
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Training and docs
How to operate, pause, and report issues with context.
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Evolution roadmap
New tools and improvements prioritized after go-live.
