AI Agent and Copilot Development, Built Into Real Work
Quantic Systems is an engineering-led AI agent development company. We build secure, permission-aware AI assistants for companies: grounded in their documents, connected to their systems, and measured before they ship. We run agents in our own products, so we know where they break.
Why most AI pilots never ship
The demo is the easy part. Production is where AI projects stall.
The pilot answers questions about a few sample files, then meets the real system. It cannot read half the documents it needs, and it quotes the other half to people who should never see them. It cannot sign in the way employees do. Nobody can tell when it is wrong. When it changes a record, nobody approved it, and when it gives a bad answer, there is no trace to explain why.
We treat a copilot or agent as production software: permissioned, tested, traced and built into the tools people already use.
Custom AI agent development
An agentic workforce for your business: copilots and agents that work on your data, inside your systems. Typical scope:
Permission-aware search and RAG
Hybrid retrieval that combines vector and full-text search with reranking. Permissions are synced from the source systems, so a user never sees an answer built from a file they cannot open.
Document intelligence and ingestion
Pipelines that turn messy PDFs, slide decks and tables into clean, versioned, searchable knowledge, and keep it current as documents change.
Multi-agent orchestration
A supervisor routes each request to specialist agents, runs work in parallel where it can, and streams the answer back. Guardrails are enforced in code, not left to the prompt.
Agents that act in your systems
Agents that create and update records in your business systems over the Model Context Protocol, acting as the signed-in user, with a confirmation step before every write and no duplicate writes on retry.
Human handoff
The assistant recognizes when to stop and passes the conversation to a person with full context, so nobody has to repeat themselves. The conversation can resume with the agent afterward.
Cross-document reasoning
Knowledge graph techniques that link people, systems and decisions across documents, for questions plain retrieval cannot answer.
Voice agents
Phone agents with live transcription, natural speech and interruption handling. They use the same knowledge, permissions and tools as your chat assistant, read back changes for a spoken yes, and transfer to a person when needed.
Assistant interfaces
An employee assistant, an admin console, and an embeddable chat widget for your website, with streamed answers and citations to the source.
Enterprise sign-in and tenancy
Single sign-on with the company account people already use, directory group sync, and tenant isolation enforced in the database.
Evaluation and release gates
A golden set of real questions and a red-team suite run on every change. A release that lowers answer quality or leaks restricted content does not ship.
Observability and cost control
A trace for every step an agent takes, cost per answer by customer, and budget alerts before spend surprises anyone.
Have a workflow in mind?
Tell us the task. We will tell you whether AI is the right tool for it.
How we keep agents reliable
Permissions in code, not prompts
Who can see what is enforced in the database and the retrieval layer. A model is never asked to keep a secret.
People confirm every write
Agents draft and suggest. Changes to real records wait for a confirmation from the person the agent is acting for.
Sensitive data stays protected
Personal and regulated data can be redacted before it reaches a model provider, and every tenant is isolated from the next.
Every step is traced
What the agent read, decided, called and spent is recorded, so you can review it, explain it and improve it.
Graded before it ships
Every change runs against a golden set and a red-team suite as a release gate, not a spot check after launch.
No lock-in to one model
We build on OpenAI and Anthropic models and keep the model layer swappable as better options appear.
How we work
- 01
Pick the workflow
We find the task where AI saves real time or catches real errors, and define what good output looks like. Data sensitivity and compliance come up here, not after launch.
- 02
Prototype on real data
We build a working version against your actual documents, systems and examples, so you judge it on your work, not a demo.
- 03
Add guardrails
Permissions, redaction, approval steps, logging and an evaluation set go in before anyone relies on the output.
- 04
Integrate and launch
The copilot or agent ships inside the tools your team already uses, with monitoring and a release process.
- 05
Monitor and improve
We review quality, cost and usage after launch, then tune prompts, retrieval and rules with the same team.
Agents we build and run ourselves
The patterns on this page come from our own products, where the agents handle real work every day.
In TasiPsych, an audit agent checks every clinical note against 42+ rules and flags what is missing. Coding suggestions come with a confidence score and the reasoning behind them. The TasiPsych Copilot, a Chrome extension, moves finished notes into the clinician's EHR in one click. Personal information is redacted before anything reaches an AI provider, and that control cannot be turned off.
Visit TasiPsych
TasiFlow includes an AI receptionist that answers calls when the front desk cannot, books appointments, takes messages and escalates what matters, plus a website chatbot that answers questions and captures leads around the clock. It is in early access.
Visit TasiFlowCustom MCP server development, proven in production
We build custom MCP servers that give agents direct, on-demand access to business data. Our own server pulls Google Search Console and Google Analytics 4 data for the agents that run our search and analytics reporting.
- Python
- Model Context Protocol
- Google Search Console API
- GA4 Data API
- Azure Container Apps
We can build the same pattern for your systems: CRMs, internal databases, or third-party APIs your team already pays to access.
Owned, not rented
It replaced a paid third-party analytics connector with a self-hosted service we control.
Hosted and on demand
A hosted HTTPS endpoint on Azure Container Apps, not a local tool. Scheduled agents call it when they need data, and it scales to zero when idle, so it costs almost nothing.
Aggregated on the server
Results are summarized before they are returned. Agents get small, analysis-ready answers instead of raw row dumps, which keeps runs fast and cheap.
No pipeline to maintain
There is no warehouse or ETL job. Comparison windows are pulled live from the source APIs at query time.
Drops into existing workflows
It plugs into the agent workflows we already run with minimal changes.
Our stack
The model is one part of the system. Most of the work is in the data access, tooling, permissions and testing around it. If you already run on a cloud or platform, we build on it.
Building a full product around it? See SaaS product development →Who this is for
Companies that want employees or customers to get answers from their own documents without exposing restricted files.
Teams whose AI pilot worked in a demo but never made it into production.
Organizations in regulated industries, such as healthcare, that need sensitive data kept out of reach.
Frequently asked questions
What does an AI agent development company do?
It designs, builds and runs AI software that does a specific job inside a business workflow. That covers choosing the task, connecting models to your data and tools, adding guardrails and logging, testing output quality, and integrating the result into the systems your team already uses. Quantic Systems does all of it end to end, then keeps improving it after launch.
What is the difference between an AI copilot and an AI agent?
A copilot works alongside a person. It drafts, suggests and answers, and the person decides what to use. An agent completes steps on its own, such as calling an API, updating a record or booking an appointment. Most of what we build mixes the two: the agent does the work, and a person approves anything with real consequences.
Can an AI assistant respect our existing document permissions?
Yes. We sync permissions from the systems your documents live in and enforce them in the retrieval layer, in code. A user only gets answers built from content they are allowed to open. Permission-negative test cases run on every release to prove it.
Can an AI agent take actions in our systems safely?
Yes. Agents connect to your systems over the Model Context Protocol and act as the signed-in user, with that user's own access. Every write waits for a confirmation, retries never create duplicates, and every action is logged.
How do you stop an agent from making mistakes?
No model is right every time, so we design for that. Answers are grounded in your documents with citations. A golden set of real questions and a red-team suite run as release gates. When the assistant is unsure or the request needs a person, it hands off with full context.
Do you offer custom MCP server development?
Yes. We build Model Context Protocol servers that connect AI assistants such as Claude to internal systems and data, with per-user sign-in, scoped access, write confirmation and logging. We run our own hosted MCP server for Google Search Console and GA4 data, which our scheduled agents call on demand.
Do you build voice agents?
Yes. We build phone agents with live transcription, natural speech and interruption handling. Voice is another channel on the same assistant, so it uses the same knowledge, permissions, tools and human handoff, with a warm transfer to your team when needed.
How much does it cost to build an AI agent?
Cost follows scope: how many systems the agent connects to, how sensitive the data is, and how much review the output needs. We do not quote from a template. After a short discovery call we give a written estimate tied to one defined workflow.
Start with one workflow
Tell us the task your team spends too long on. We will tell you how we would approach it and what a first release looks like.