AI delivery
What AI Automation Actually Looks Like for a Small Business
What we build for SMB teams in Georgia: focused AI features tied to clear operational wins.
Start with one workflow where your team loses hours every week.
Pair AI with rules and approvals so outcomes stay reliable.
Ship in phases and measure impact before expanding scope.
Use case framing and data
Start with one workflow where your team loses real hours each week, then target a narrow AI use case with measurable impact.
Ground outputs using your own SOPs, job history, and customer data so responses stay useful for day-to-day operations.
- Retrieval pipelines with quality gates and PII redaction.
- Latency and cost budgets per intent; caching where safe.
- Evaluation sets mapped to acceptance criteria per use case.
Architecture and safety
RAG-first patterns, tool use where needed, and deterministic rails for money-or-compliance steps.
Safety layers: allow/deny lists, toxicity/PII filters, and grounding checks.
- Observability: traces/logs with correlation IDs back to users.
- Versioned prompts/models with feature flags.
- Data residency and access controls by tenant/region.
Operate and evolve
Monitor quality, drift, latency, and cost; run post-incident reviews for AI failures.
Keep a change log and rollback path for prompts, embeddings, and model versions.
- Shadow and canary new models.
- Feedback loops from users and human-in-the-loop review for low confidence.
- Retention and deletion policies for logs and embeddings.
Build with us
Have a workflow, app, or integration in mind?
Tell us what you want. We will share a plan, staging timeline, and a fair price you can see.