Ship AI that earns its place in the product, not the pitch deck.
Algoramming is an AI development company in Dhaka, Bangladesh that builds AI features, automations, chatbots, and predictive analytics into production software for clients across the UAE, Qatar, Saudi Arabia, the US, the UK, and Australia. One senior team handles integration, guardrails, and evaluation, and you own the code and the data from the first commit.
We build AI into the software you actually run: features grounded in your own data, automations that remove repetitive work, and assistants that know their limits, all measured so quality is a number rather than a hope.
One senior team, one playbook, applied to the specialism your project actually needs. Each opens a full brief of its own: outcomes, deliverables, and the questions teams ask before signing.
Put AI inside the product, where it earns its keep.
Explore ai integrationAutomate the repetitive work that eats your team's week.
Explore workflow automationAssistants that answer from your data and know their limits.
Explore chatbots and agentsTurn your data into forecasts people actually use.
Explore predictive analyticsA long-form view of what this service line actually means in practice, written for the people who buy it and the people who will live inside the product after launch.
Nearly anything looks impressive in a first demo. The value is in what happens next: real, messy inputs, edge cases, and the cost of a confident wrong answer. We build AI features on the model that fits the job, ground them in your own data through retrieval, and wrap them in guardrails so they fail safely rather than loudly. The point is a feature people come to rely on, not a screenshot for the pitch deck.
The best automation targets are repetitive and rule-based: moving data between systems, generating routine documents, triaging standard requests. We map where your team loses hours to that kind of work and automate it, keeping a person in the loop for anything that needs judgement. Every automation is logged and monitored, so it is trustworthy enough to leave running rather than one more thing someone has to check.
AI behaviour is a distribution, not a yes or no, so we treat evaluation as part of the build. An evaluation harness tracks how often the system is right, where it goes wrong, and whether a prompt or model change actually helped. That is what makes it possible to ship AI responsibly and improve it deliberately, instead of shipping a black box and hoping. We are also honest about the cases where the mature answer is to not use AI at all.
If one of these sounds like your situation, the rest of this page is calibrated for you. If none does, send a note anyway: we will tell you whether we are the right partner.
Teams with a real use case for AI, not a mandate to add it somewhere
Operations leaders drowning in repetitive, rule-based work
Products that sit on valuable data they have never turned into leverage
We measure success in shipped value, not Jira tickets closed. Every engagement is anchored to a small number of outcomes both sides can defend.
AI features that solve a real problem and hold up on messy input
Repetitive work automated, with humans kept in the loop where it counts
Answers grounded in your data, with quality measured by evaluation
Cost, latency, and safety kept inside limits the product can afford
A clear-eyed view of where AI helps and where it honestly does not
These are the tangible things that land in your accounts at the end of an engagement. Everything lives in your repositories, your cloud, and your control from day one.
AI features integrated into your product, grounded in your data
Automations across your existing tools, logged and monitored
Guardrails, prompt design, and human-in-the-loop checks
An evaluation harness that tracks quality over time
Source code and model configuration in your accounts
Match the engagement model to where your problem is today. The team, the cadence, and the standards do not change between shapes.
Fixed scope · fixed price
Discover, design, build, launch, and hand over. One number, one date, one team accountable from kick-off to keys.
Best for: Clear brief, real deadline.
Monthly · rolling backlog
A fixed senior allocation each month against a re-ranked backlog. Ship what matters every two weeks and review the plan together.
Best for: Priorities shift faster than annual plans.
Per-engineer · monthly
We embed in your Slack, your repos, and your sprint board, on-camera daily, treated as your team for the duration.
Best for: In-house engineers who need senior horsepower.
The same playbook on every project. Boring, on purpose. Predictable, by design. The bead below traces the path we walk together, every time.
We pin down the use case, the data available, and the cost of getting an answer wrong before building anything.
We design the retrieval, prompts, guardrails, and the human-in-the-loop points that match the risk.
We integrate the feature or automation into your product and stand up an evaluation harness alongside it.
We ship behind flags, watch quality, cost, and latency on real traffic, and tune before widening.
We monitor accuracy as the world drifts, retrain or re-prompt as needed, and keep the guardrails honest.
The 10 tools below are the everyday kit our team reaches for on this service line. None of them are sacred; every choice is justified against the problem in front of us.
Have a different one? Send a single email; we usually answer within a business day.
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See the workPlain-English reply within one business day. NDA on request. Discovery call is free.
