Service
AI and data engineering that adds practical intelligence to your product
Add AI features to existing products: LLM integrations, document extraction, forecasting, and the data pipelines and evaluation that make them reliable.
Why teams choose this service
We help teams add AI where it improves a measurable workflow — classifying requests, extracting data from documents, summarizing information, forecasting demand, or flagging anomalies. Every feature starts with a data assessment and an evaluation set, ships with human review where accuracy matters, and is monitored in production for quality and cost. Behind it we build the pipelines and data models that keep inputs clean and governed.
What this engagement typically includes
- Use-case and data readiness assessment tied to a business metric
- LLM or ML feature integrated into your existing app or workflow
- Evaluation sets, human-in-the-loop review, and production monitoring
- Data pipelines, quality checks, and access controls for the data involved
Relevant case studies
We don't yet have a published case study specific to this stack. Browse all of our work or talk to us about similar projects we've delivered under NDA.
Related guides
- How to Build an AI Transformation Roadmap for Mid-Market Companies
A practical roadmap for moving from AI curiosity to execution — with clear priorities, governance, and measurable results.
- Common Mistakes in AI Projects and How to Avoid Them
A practical guide to the mistakes that derail AI projects — and the habits that prevent them.
- Data Governance Framework for Modern Enterprises
How modern companies can create a data governance framework that supports growth, compliance, and better decisions.
Service FAQ
Frequently asked questions about AI & Data Engineering
Helpful answers for teams evaluating a delivery partner for this capability.
How do you approach AI & Data Engineering projects?
We start with a short discovery phase to confirm scope, architecture, and success criteria, then deliver with Python, LLM APIs, Data Pipelines, and structured QA — with the same team from kickoff through launch.
What does AI & Data Engineering typically cost, and how is it priced?
It depends on scope and engagement model. We offer fixed-scope pricing for well-defined builds, a dedicated-team model for ongoing work, and time & material for evolving requirements — see our engagement models and typical budget ranges on the pricing page for detail.
How long does a typical project take?
Most engagements run 8-16 weeks from discovery to launch depending on scope, with dedicated-team engagements continuing beyond launch for ongoing feature work. We confirm a realistic timeline during discovery before development starts.
Do you sign an NDA before discussing project details?
Yes. We routinely work under NDA and treat confidentiality as the default, especially for early-stage products and regulated industries.
Ready to discuss your next software project?
We help teams scope, build, and launch reliable software — from native mobile apps and modern web platforms to enterprise systems and long-term support.
info@hakrosoft.com · We respond within one business day