Micromarketing designs practical AI systems using LLMs, assistants, chatbots, retrieval, and automation patterns that fit real business workflows.
AI
AI Strategy and Systems
We design and build AI copilots, agent workflows, retrieval systems, and AI-powered product features that help teams move faster without losing control.
01 Context Docs + data
02 AI reasoning Copilot + agents
03 Review gates Human control
04 Outputs Tools + products
AI systems, not experiments
Workflow-first Grounded in your data Human review built in Measured after launch
AI capabilities
From internal copilots to AI-powered products.
AI copilots
Internal assistants for drafting, research, support, sales prep, reporting, summarization, and task acceleration.
Agent workflows
Goal-driven workflows that gather context, call tools, route outputs, and keep humans in control at key checkpoints.
Knowledge and retrieval
RAG, structured knowledge bases, document search, content QA, and business-specific memory for more accurate outputs.
AI product features
Embed generation, classification, recommendations, enrichment, and automation into customer-facing apps and dashboards.
Operational safeguards
Context, control, and measurement are part of the build.
AI becomes dependable when the system knows what it can use, when a person must review the work, and how success or failure will be measured.
Grounded context
We connect AI systems to the right business data, documents, workflows, and permissions instead of relying on generic prompts.
Human checkpoints
Approvals, review queues, logs, and escalation paths keep AI useful without letting it silently make risky business decisions.
Measurable outcomes
We track time saved, response quality, conversion lift, cost per task, and failure modes so the system improves after launch.
How we build it
A clear path from promising idea to reliable AI system.
Each stage produces something your team can review, test, and use to make the next decision with confidence.
Map the highest-value use case
We document the workflow, users, decisions, source data, current effort, and failure cost before recommending an AI approach.
- Workflow and data map
- Success baseline
- Risk and permission boundaries
Prove quality with real examples
We build the smallest useful interface, prompt and retrieval path, then evaluate it against representative business scenarios.
- Working prototype
- Evaluation examples
- Fallback behavior
Connect tools and human review
The approved workflow is integrated with the systems it needs while permissions, review gates, logs, and operator controls are added.
- System integrations
- Approval checkpoints
- Usage and quality analytics
Measure, improve, and expand
We monitor quality, adoption, speed, cost, and exceptions so the system improves from evidence instead of assumptions.
- Performance dashboard
- Feedback loop
- Expansion roadmap
Applied AI systems
Four practical ways AI can show up in daily operations.
Support assistants
Answer customer questions, summarize cases, route requests, and keep human teams focused on exceptions.
Workflow automation
Move data between forms, CRMs, dashboards, approvals, and reports without repetitive manual handoffs.
Knowledge retrieval
Search documents, SOPs, product details, policies, and internal notes with business-specific context.
Revenue intelligence
Turn ecommerce, campaign, sales, and customer behavior data into recommendations and next-best actions.
Ready to make AI operational?
Let’s turn your best AI use case into a reliable workflow.
We’ll identify the highest-value path, required data, safeguards, and build plan.
Start AI Discovery