Iterative AI Agent Deployment

We help companies design, build and run supervised multi-agent systems. Our products:

  • Victoria Customer support agent replacing 60% of support staff at spartans.com.
  • Harvey Trained against the complete UAE law database, 15,000+ laws, to help Dubai law firms.

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Victoria live at spartans.com1 yearanswering customers in the site's chat
Conversations handled100,000and counting
Support staff replaced60%at spartans.com
Laws in Harvey's database15,000+UAE, indexed and searchable

Victoria: replies to customers with their complete account information, fetched from your database.

Victoria is a general-purpose AI agent that we tune for each business, for example spartans.com, an online casino and sportsbook with more than a billion dollars in revenue. Every message from a customer makes Victoria think about which information she needs. She then fetches it from the spartans.com knowledge base or calls internal endpoints for the customer's account, balance and bonuses, and answers in the customer's language. A second AI checks every reply before it is sent, and a person can take over the chat at any point.

A message from LiveChat is pre-processed, the AI plans what it needs, fetches from the knowledge base or the back-office API, formats the reply, a second AI supervises it, and the reply goes back to LiveChat; the channel can be swapped for Telegram, an API or MCP swappable with Telegram any API MCP LiveChatspartans.com chat Pre-processverify, dedupe, batch Planwhat is needed Knowledge basevector database spartans.com cloud Back-office APIaccount, wallet, bonuses external, called as needed Formatwrites the reply Supervisechecks the reply ok a person can take overat any step the checked reply goes back to the customer in LiveChat

Diagrams scroll sideways on small screens.

Knows the customer

Profile, wallet, recent transactions and active bonuses are fetched from your systems when the chat starts, so Victoria answers with real numbers.

Speaks any language

English, Russian, Japanese and more, detected from each message, with the same tone and the same links.

People stay in control

A second AI checks every reply, and your team can take over any conversation at any step.

Harvey: legal research for Dubai law firms, built on the complete UAE law database.

Ask Harvey a legal question and it answers with the exact articles of law the answer rests on. More than 15,000 UAE laws, from the Commercial Companies Law to VAT, arbitration, data protection and VARA, are indexed in its database. A lawyer reviews every answer.

A legal question: Harvey writes search queries, searches a vector database of more than 15,000 indexed UAE laws, searches again if it has not found enough, answers with the articles cited, and a lawyer reviews the answer Legal questionfrom the lawyer Write querieswhat to search for Search the lawsmost relevant articles Enough law found?if not, search again search again Answerwith the articles cited a lawyer reviews everyanswer Vector database: 15,000+ indexed UAE laws

Every answer cited

Harvey names the law and the article each point comes from, and lists missing facts, tactics and pitfalls.

Searches until it has enough

If the first search does not cover the question, Harvey writes new queries and searches again before answering.

Any AI model

Has run on Gemini, Claude and DeepSeek without any change to Harvey itself.

One architecture behind every agent we build.

A message comes in from your website chat, Telegram or another channel. The agent plans what it needs, fetches it from your knowledge base or your systems, writes the reply, and a second AI checks it before anyone sees it. A person can approve, edit or take over at any point. The same design handles customer questions, document intake and drafting in any regulated business.

A message from any channel passes through planner, formatter, responder and supervisor, past a person, and back to the channel; the agent talks both ways with your back-office API and your knowledge base Any channel LiveChat Telegram Website chat API or MCP The agent Plannerdecides what to fetch Formatterbuilds the context Responderwrites the reply okSupervisorchecks the reply a person approves, edits,or takes over Conversation memory: settings and the customer's account context Your systems Back-office API Knowledge base the checked reply goes back to the channel it came from

Every change is tested before your customers see it.

Before an agent goes live it runs through our harness: every test case, every reply scored, round after round while we tune prompts and settings. The version that scores best is the one that ships. Nothing reaches customers on a hunch.

In production every reply is recorded with what the agent looked up and why it answered as it did, so any conversation can be explained afterwards.

Test cases go into the harness, which runs every case and scores every reply; we tune the agent and the harness runs again, round after round, until the final version ships Test cases Harnessruns and scores every case Tune the agentprompts and settings round after round Final version shipswhen the scores are good

Works with any AI provider.

Any model can be swapped in on the same architecture, with no rebuild. We support A/B testing with complete telemetry, so you can see cost against performance for each model and make informed decisions.

Agent code asks the registry for a role; the registry returns the configured provider Agent codeasks for "chat" Registryrole to provider Claude Gemini DeepSeek OpenAI

Your data stays yours.

Your own deployment

Each client runs on its own isolated stack, on our servers or on your cloud, encrypted from the first request.

No training on your data

We use the AI providers under settings that keep your data out of model training.

Every conversation explainable

Each reply is logged with what was looked up and why, so you can always answer the question of why it said that.

People where you want them

Approval before any reply, before any change to your systems, or a handoff that sends the conversation to your team.

Try our agent.

This chat is built on the same architecture as Victoria and Harvey. Ask how they work, what a project with us looks like, or anything on this page. The record beside it shows how each answer was produced. To reach a person, hand the conversation over and it goes straight to our team.

AlgoPy Technologies agentup to 20 turns this session
Hi. I can explain how Victoria and Harvey are built, what the reference architecture looks like, and how an engagement works. What would you like to know?
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A working agent in about four weeks.

We connect one channel and one of your systems, build the agent with checks and human approval where you want them, test it against your real cases, and hand you a dashboard. Then we keep improving it in production, or your team does. We also work as engineers inside teams building their own agents.

Weeks 1 and 2

Connect

Your chat channel, one of your systems, and your documents loaded into the agent's knowledge base.

Week 3

Build and test

The agent, its checks and human approvals, tested against real cases written with your team.

Week 4

Go live

Dashboard, runbooks and a clear monthly cost. We run it for you, or your team takes over.

Next approval
yours
Talk to us himanshuclash@gmail.com

We reply within a working day. Or ask our agent first; it can hand the conversation to a person.

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AlgoPy Technologies, 2026.
AlgoPy TechnologiesTry our agent