AI Agent DevelopmentWe turn AI agentsinto products
Demos are easy. Products people use every day are not. We design the conversation flow, the memory, the retrieval, and the tool calls — then build the AI service and operate it ourselves.
So it stops at the demo
We often hear that a team tried AI but could not get it into a real service. The reason is usually one of three. We design for all three from the start.
"Once the conversation gets long, it forgets what was said."
We design the memory first
Chat history and long-term memory are stored separately, and every turn decides what to read and what to write. Remembering a user across sessions is a design outcome, not a model capability.
"It sounds convincing, but there is nothing behind it."
We make it search before it speaks
Vector search finds the documents an answer should rest on, and the agent speaks only from what it found. When you can trace which document produced which answer, you can fix the wrong ones.
"As soon as numbers or rules come up, it gets them wrong."
We never leave arithmetic to the model
Rule-bound domains become separate, verifiable calculation engines, and the agent calls them as tools. The engine owns the calculation; the model owns the explanation.
How the agent works
This is not a diagram drawn to explain something. It is what runs right now in a service we operate ourselves.
Input
Takes the question and the conversation so far
Plan
Decides what has to be answered and picks the tools
Tool call
Calls calculation engines and backend APIs for facts
Memory
Reads past and long-term memory, and writes this turn
Answer
Builds the answer on those facts and streams it back
Conversation graph
Branches and retries are defined as a graph, so you can retrace what happened at which step.
LangGraphLong-term memory
Chat history is kept per session; what the agent learns about a user goes into long-term memory. The graph decides what to read back on each turn.
MongoDBDocument retrieval
When a question needs grounds, the agent searches the document store first. Which documents fed an answer is logged.
QdrantPrompt management
Prompts live as data, not code. They can be changed without a deploy, and what changed when is recorded.
Prompt Registry
Each service owns its own graph. Changing one service does not shake the others.
How we work
What happens after you get in touch, and in what order.
Inquiry
Tell us what you want automated. An early idea is fine. We reply within one or two business days.
Scope
We decide what the agent will answer and what it will not. This is also where we tell you what today's technology cannot do.
Agent design
We settle the conversation graph, what to remember, which tools to call, and which documents to retrieve. What we agree here becomes the build spec.
Build
The agent and the product are built together. Progress is shared as a screen you can actually talk to.
Launch
We ship to production and wire up conversation logs, response latency, and error visibility.
Operate
We read real conversations and tune the prompts and retrieval. Answer quality rises here, not at launch.
What we can build
We design the agent, build the product it runs inside, and turn rule-heavy domains into calculation engines the agent can call.
AI agent development
We design and build agents with conversation flow, long-term memory, document retrieval, and tool calling — so you can trace where an answer came from.
AI product build and operation
One team builds the web, app, and admin tools the agent runs inside. We do not hand it over and leave; we operate it.
Domain engines
We move rule-heavy domains, such as Saju and astronomy, into verifiable calculation engines and expose them as tools the agent can call.
What is different
We compared approaches, not companies. Which one is right depends on the problem you are solving.
| Criterion | Prompt chatbot | NewRoute | General SI build |
|---|---|---|---|
| Conversation design | A single prompt | Branches and retries defined as a graph | Built to the requirements spec |
| Grounds for an answer | Whatever the model knows | Finds documents first, answers only from them | Depends on the setup |
| Calculation and rules | Left to the model | A verifiable engine calculates; the model explains | Built as a separate module |
| Remembering the user | Gone when the window closes | Long-term memory stored apart, kept across sessions | Built if it is in the requirements |
| Changing a prompt | Edit code and deploy | Managed as data; changed without a deploy, with a change record | Change request, then deploy |
| After launch | Left as is | The same team reads real conversations and tunes answer quality | Handled within the maintenance contract |
A working demo takes a day. Being used daily is the work after that.
Our work starts where the demo ends. We read real conversations to find where answers wobble, rethink what to remember and what to retrieve, and fix the order in which tools are called. Because we operate what we build, this is a standard we hold ourselves to, not just one we ask of others.
Is there an agent you want built?
Tell us what you want automated, or what questions it should answer. We will map out what is possible and what today's technology still cannot do.
Contact us