Assistants that answer from your data and know their limits.
Algoramming builds chatbots and AI agents from Dhaka, Bangladesh for teams across the UAE, Qatar, Saudi Arabia, the US, the UK, and Australia. We create assistants that answer from your own content through retrieval (RAG), take safe actions through your systems, and hand off to a human when they should, so they help customers rather than embarrass the brand.
We build chatbots and AI agents that answer from your own data, take safe actions through your systems, and hand off to a person when they reach the edge of what they should do.
AI integrationWorkflow automationPredictive analytics
DisciplineAI & automation
CadenceTwo-week shipping rhythm
TeamSenior engineers & designers
Source codeYours from commit one
AI & automation
Discipline
AI & automation
Cadence
Two-week shipping rhythm
Team
Senior engineers & designers
Source code
Yours from commit one
A deeper look
Everything to know about chatbots and agents.
What this specialism means in practice, written for the people who buy it and the people who will live inside the product after launch.
Covered end to end4
01
RAG-grounded first, clever second
A chatbot that improvises answers is a liability. We ground assistants in your actual content, your documentation, policies, and product data, so their answers trace back to something true. When a question falls outside what they know, they say so and hand off rather than inventing a confident guess. That restraint is exactly what makes an assistant safe to put in front of customers, and it is the part most rushed builds skip.
02
From answering to acting, carefully
The real value often comes when an assistant can do something, check an order, book a slot, update a record, not just talk about it. We connect agents to your systems through well-defined, permissioned tools, so their actions are constrained to what they are allowed to do and logged for accountability. Higher-risk actions sit behind confirmation or human approval. The result is genuinely useful, without handing an unpredictable model the keys.
What you get
Outcomes, not deliverables.
We measure success in shipped value, not tickets closed. Every engagement is anchored to a few outcomes both sides can defend.
Two-week shipping rhythm
01
An assistant that answers accurately from your content
02
Deflection of routine questions, freeing your team
03
Safe, permissioned actions rather than a chat-only toy
04
A clean handoff to a human when it matters
What we deliver
Concrete artifacts, not slide decks.
Everything lands in your repositories, your cloud, and your control. Nothing is locked behind us.
01
Artifact
A chatbot or agent grounded in your knowledge base
02
Artifact
Safe tool and system integrations for real actions
03
Artifact
Guardrails, fallback, and human handoff
04
Artifact
Analytics on questions, deflection, and gaps
Our process
The same senior team, the same playbook.
Chatbots and agents runs on the ai & automation playbook. Boring on purpose, predictable by design.
01Discover
→
02Design
→
03Build
→
04Launch
→
05Support
1
Discover
We pin down the use case, the data available, and the cost of getting an answer wrong before building anything.
Phase 01 / 05
2
Design
We design the retrieval, prompts, guardrails, and the human-in-the-loop points that match the risk.
Phase 02 / 05
3
Build
We integrate the feature or automation into your product and stand up an evaluation harness alongside it.
Phase 03 / 05
4
Launch
We ship behind flags, watch quality, cost, and latency on real traffic, and tune before widening.
Phase 04 / 05
5
Support
We monitor accuracy as the world drifts, retrain or re-prompt as needed, and keep the guardrails honest.
Phase 05 / 05
Our toolkit
Pragmatic tools. Senior judgement.
The everyday kit our team reaches for on this work. None of it is sacred; every choice is justified against the problem.
Interface
Application
Data and APIs
Cloud and delivery
01Python
02TypeScript
03OpenAI
04Anthropic
05LangChain
06pgvector
07n8n
08Zapier
09Sentry
10Datadog
Common questions
Things teams ask before signing.
Have a different one? Send a single email; we usually answer within a business day.
01How do you build a chatbot grounded in our own data?
We use retrieval-augmented generation (RAG): your documentation, policies, and product data are indexed, and the assistant retrieves the relevant passages before it answers, so its replies trace back to your real content rather than the model's general training.
02How do you stop the chatbot from hallucinating?
By grounding it in your content through retrieval, constraining it with guardrails, and having it defer to a human when it is unsure rather than guess. We also monitor real conversations to find and close gaps, and are honest that this reduces confident errors sharply without removing them entirely.
03Can the agent take real actions, not just answer questions?
Yes. With safe, permissioned integrations an agent can take real actions in your systems, checking an order, booking a slot, updating a record, with higher-risk steps gated behind confirmation or human approval and every action logged.
04Where can we deploy it?
On your website, in your product, or inside tools like your support desk or messaging channels, wherever your users already are.