Put AI inside the product, where it earns its keep.
Algoramming provides AI and ML integration services from Dhaka, Bangladesh for teams across the UAE, Qatar, Saudi Arabia, the US, the UK, and Australia. We build features on top of large language models and, where it fits, custom models, with the retrieval, guardrails, and evaluation that make AI dependable in production rather than impressive only in a demo.
We build AI features into your product, on top of the right models, with the retrieval, guardrails, and evaluation that separate something dependable from a clever demo.
Workflow automationChatbots and agentsPredictive 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 ai integration.
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
AI and ML integration where the demo is the easy part
Almost anything looks impressive in a first demo with a capable model. Production is where AI features earn or lose trust. The hard, valuable work is grounding answers in your own data through retrieval, designing prompts and guardrails so the system fails safely, and controlling cost and latency so the feature is affordable at scale. We build for that reality, so the AI holds up on the messy, real inputs your users will actually throw at it.
02
If you cannot measure it, you cannot ship it responsibly
AI features are probabilistic, which means quality is a distribution, not a yes or no. We build an evaluation harness for the behaviour that matters, so you can see how often the system is right, where it goes wrong, and whether a prompt or model change actually helped. That turns AI from a black box you hope is working into a feature you can improve deliberately and defend to stakeholders.
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
AI features that solve a real user problem, not a novelty
02
Answers grounded in your data, with fewer confident mistakes
03
Guardrails and evaluation so quality is measured, not hoped
04
Cost and latency kept within limits the product can afford
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
AI feature built into your product
02
Artifact
Retrieval over your own data where relevant
03
Artifact
Guardrails, prompt design, and safety handling
04
Artifact
An evaluation harness to measure and track quality
Our process
The same senior team, the same playbook.
AI integration 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.
01Can you integrate an LLM into our existing software?
Yes. That is the common case: we add LLM-powered features into a product you already have, connect them to your data through retrieval, and keep the integration flexible so you can change model or provider later without a rewrite.
02RAG or a fine-tuned model, which do we actually need?
Usually retrieval-augmented generation, because it grounds a strong existing model in your own data without the cost of training. Fine-tuning makes sense for a fixed style or a narrow task with good examples. Often the right answer is RAG first, fine-tuning only if it is still needed.
03How do you keep the AI from making things up?
We ground answers in your data through retrieval, constrain the model with guardrails, and measure accuracy with evaluations. That reduces confident errors sharply, and we are honest that it does not remove them entirely.
04How do you evaluate and track AI output quality over time?
We build an evaluation harness for the behaviour that matters, so you can see how often the system is right, where it fails, and whether a prompt or model change actually helped. Running it continuously catches quality drift before your users do.
05Which AI providers do you build on?
We choose the model that fits your quality, cost, privacy, and latency needs, and keep the integration flexible so switching providers later is a contained change.
