AI & Data Solutions
AI that earns its keep — measured in outcomes, not demos.
From LLM-powered assistants to predictive models and data platforms, we ship AI that moves a business metric you already track.
Overview
What AI and data solutions really are
AI and data solutions are systems that turn the data you already generate into decisions, predictions, and automation — reliably enough to run in production. The test is simple: an AI project succeeds when it moves a metric you already report on, and fails when it only demos well.
Asteriskk Technologies builds the whole chain: pipelines that clean and organise your data, models and LLM integrations that act on it, and dashboards that show the impact. We start every engagement with a two-week feasibility sprint against your real data, so you know the achievable accuracy and the projected ROI before committing to a build.
Recent results: a support assistant deflecting 45% of tickets at equal satisfaction scores, a demand-forecasting model cutting stockouts 30%, and document pipelines saving teams hundreds of review hours monthly. Measured, not hyped.
Capabilities
What we deliver
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LLM integration & assistants
Retrieval-augmented assistants grounded in your own documents and data — with guardrails, evaluation, and audit logs built in.
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Predictive models
Forecasting, scoring, and anomaly detection trained on your history — demand, churn, credit, fraud — deployed behind clean APIs.
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Data pipelines & warehousing
Reliable ELT pipelines and modelled warehouses that end the era of five conflicting versions of every number.
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Business intelligence
Dashboards your executives actually open — one agreed source of truth for the metrics that run the company.
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Computer vision
Image and document processing that automates inspection, extraction, and verification at accuracy levels humans cannot sustain.
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MLOps & model monitoring
Versioning, drift detection, and retraining pipelines that keep models accurate long after launch day applause.
Process
How the work runs
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01
Prove feasibility
A two-week sprint against your real data answers the only question that matters: will this work well enough to pay off?
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02
Build the data foundation
Pipelines, quality checks, and governance first — because no model outruns bad data.
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03
Ship the model
The model or assistant goes live inside a real workflow, with humans in the loop wherever stakes demand it.
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04
Measure and improve
Every prediction is logged against outcomes, feeding retraining cycles and an honest, running ROI figure.
Tech stack
Technologies we use for this
Python
OpenAI
TensorFlow
PostgreSQL
MySQL
Redis
Google Cloud
AWS
FAQ
Questions buyers ask us
Usually more than you think, and the feasibility sprint settles it in two weeks with evidence. If gaps exist, we tell you exactly what to start capturing and how long until a model becomes viable — before you spend on a build.
For language tasks, adapting foundation models with your data via retrieval wins on cost and speed 90% of the time. Custom training pays off for narrow, high-volume prediction tasks — the feasibility sprint tells you which side you are on.
Your data is never used to train third-party models. We use enterprise API tiers with zero-retention agreements, or deploy models inside your own cloud when regulation requires it, with access controls and audit logs throughout.
A first production use case typically ships in 8–12 weeks after feasibility, with impact visible in the following quarter. We define the target metric and its baseline before the build starts, so ROI is a measurement, not a debate.
The feasibility sprint is a fixed $7,500. Production builds typically range from $30,000 to $150,000 depending on data readiness and integration depth — and the sprint gives you that number for your specific case before you commit.
Have data doing nothing? Put it to work.
Start with a two-week feasibility sprint — real answers about accuracy, cost, and ROI before you invest in a build.