AI Assistants
& Agents
We build AI agents, private knowledge systems, RAG solutions, and MCP integrations that answer from your product docs, policies, and order data with sources, use tools under permission, and hand risky actions to a human for approval. We also build these features into the store, marketplace, SaaS, or internal app you already run: order-aware support, smart search, and document reading. Measured before launch, monitored after.
Knowledge Locked In Too Many Places
Teams lose time when policies, docs, product knowledge, and past answers are scattered, and generic chatbots make things worse by answering confidently from nothing.
Scattered Company Knowledge
Policies, docs, guides, contracts, tickets, and SOPs live in too many places, so people waste time searching or asking the same questions.
Chatbots That Make Things Up
An assistant that answers without sources, or invents an answer when it does not know, is a liability. Ours cite where the answer came from.
Answers Not Connected To Data
A useful assistant needs to read your real records, docs, and systems, not a frozen copy from three months ago.
Agents Acting Without Limits
An agent that can call tools and change data needs permissions, logs, approval gates, and hard limits before it goes near production.
Wrong Model For The Job
Some questions need a strong LLM. Others run fine on a smaller or local model. We match the model to the task and the budget.
Unclear AI Cost
Without model choice, caching, and limits, a knowledge assistant can get expensive fast. We build cost controls in from the start.
Agents And Knowledge With Guardrails
AI systems that retrieve your knowledge, answer with sources, use tools under permission, and hand risky actions to a human.
RAG Knowledge Assistants
Private assistants that answer from your documents, databases, policies, product docs, and past tickets, with a source link on every answer.
AI Agents
Agents for support, research, operations, and document review that follow a defined task, use approved tools, and stop for human review on risky steps.
AI Search And Document Reading
Smarter search across products, policies, and records, and documents read into structured fields with a person reviewing what matters.
MCP And Tool Integrations
Custom tools and MCP servers that let AI systems safely read data, call your APIs, and work with the systems you already run.
Evals And Monitoring
A test set built from your real questions and data, measured before launch and re-run every month, so you know how often it is right.
From Knowledge Source To Grounded Answer
We design the assistant around your data, the questions people actually ask, retrieval quality, and safe tool use.
Knowledge And Data Audit
We map the documents, records, and systems the assistant should know, the questions it must answer, and the data it must never expose.
Retrieval Blueprint
We define chunking, embeddings, retrieval and reranking, source citation, agent roles, tools, approval gates, and cost controls.
Agent And RAG Development
We build ingestion, retrieval, the answer layer, tools, the widget or dashboard, and the guardrails around it.
Evaluation And Safety Testing
We test answer accuracy, grounding, retrieval quality, tool permissions, privacy, and prompt-injection resistance against a real question set.
Launch And Improve
We deploy, watch real questions, tune retrieval and prompts, track cost per answer, and expand the knowledge base over time.
Agentic AI And RAG Stack
We combine agent frameworks, retrieval systems, vector search, API tools, and backend infrastructure.
Agent Frameworks
AI And Models
RAG And Data
Infrastructure
Where Grounded AI Earns Its Place
Cases where answering from trusted data, with sources, beats a generic chatbot.
Order-Aware Support Assistant
Answers order and product questions from your real order data and help docs through fixed lookups, never a guess, and hands refunds and complaints to a person.
Store & Marketplace Back Office
AI inside the store or marketplace you already run: ticket triage, vendor onboarding checks, and supplier document reading, with approvals where it matters.
Document Review Agent
An agent that reads a document, pulls the fields that matter, checks them against your rules, and flags what a human should look at.
Specialized Tracks
What It Costs
Credited in full to any sprint booked within 30 days.
Compare engagement modelsHave a Custom Project?
Let's discuss how we can engineer a bespoke platform, multi-vendor marketplace, or AI system tailored for your business.