02 — Applied intelligence
AI agents and generative systems wired into the tools you already run
We build AI that does work, not demos. That means agents grounded in your own documentation, evaluated against real tickets, and deployed with logging, guardrails, and a human escalation path. Aimlet uses the same stack internally, including the retrieval assistant on this site.
Who it's for
Who we build this for
Best for teams drowning in repetitive support, sales, or document work, and for founders who need an AI-enabled MVP in weeks rather than quarters.
- 2–6 weeks
- From discovery to a grounded, evaluated AI pilot
- 100%
- Of agent answers logged with their retrieved sources
- Human-in-loop
- Escalation built into every production deployment
Capabilities
What's included in ai & emerging tech
Each capability can be bought on its own or bundled into a single engagement with one point of contact.
- 01
AI Agents & Workflow Automation
Agentic systems that triage tickets, qualify leads, and complete multi-step back-office tasks.
- 02
Generative AI Integration (RAG & Copilots)
Retrieval-augmented assistants grounded in your documents, so answers cite your content instead of guessing.
- 03
AI Chatbots & Voice Agents
Web, WhatsApp, and phone agents that handle first response and hand off cleanly to a person.
- 04
AI-Powered Marketing Automation
Lifecycle campaigns, segmentation, and creative variants generated and tested continuously.
- 05
MLOps & Model Deployment
Versioning, evaluation harnesses, monitoring, and cost controls for models in production.
- 06
AI Search / AEO Readiness
Structuring content and schema so ChatGPT, Gemini, and Perplexity can quote your business accurately.
- 07
No-Code / Low-Code + AI App Builders
Rapid MVPs assembled on modern AI builders, then hardened into production code when validated.
- 08
Intelligent Document Processing
Invoice, contract, and form extraction with confidence scoring and human review queues.
Process
How the work runs
- 01
Discover
Map the workflow, volumes, and the cost of each manual step.
- 02
Design
Choose model, retrieval strategy, guardrails, and escalation rules.
- 03
Build
Ship a grounded prototype and evaluate it against real historical cases.
- 04
Launch
Roll out behind a human review gate, then widen automation.
- 05
Grow
Monitor accuracy and cost per task, and retrain on new data.
Tools & tech
- OpenAI
- Google Gemini
- Anthropic Claude
- LangChain
- Vector databases (pgvector, Pinecone)
- Supabase
- Python
- TypeScript
- n8n / Make
- MLflow
Related reading
Mini FAQ
AI & Emerging Tech questions
Tell us what you need
A short brief is enough to start. We reply within one business day.