AI & data engineering

Disconnected systems create duplication, delay, and risk. We design and implement pipelines that bring together structured and unstructured data from across your stack: ERPs, OMS, CRMs, commerce, IoT, and custom tools. Using tools like Apache Airflow, Azure Data Factory, and AWS Glue, we build resilient data flows for both batch and real-time ingestion. Whether it’s cleansing inconsistent records or enriching datasets across sources, we make sure your data lands where it’s supposed to, ready for action. The result? No more guessing which dashboard to trust. Just the right data, in the right place, at the right time.

When data lives in too many places, reporting becomes a manual mess. We help consolidate your data infrastructure - whether that means centralizing into a modern warehouse (like Snowflake, BigQuery, or Redshift) or designing a hybrid lakehouse model for scale. We don’t just stand up tools. We architect with performance, cost efficiency, and governance in mind, so your platform supports BI, machine learning, and real-time analytics without spiraling into technical debt. It’s not about choosing one tool. It’s about choosing the right architecture to grow with you.

Data without governance is noise. We implement frameworks that give your teams control over what’s flowing in, where it’s stored, and how it’s used. That includes defining data ownership, access roles, and policies, along with automated checks to flag quality issues, outliers, and anomalies before they spread. We use AI-driven profiling and lineage tracking so you know what’s trustworthy and where it came from. It’s the difference between acting on insights versus assumptions.

We build dashboards that don’t just report data; they drive action. From C-suite scorecards to operational drilldowns, our interfaces are built around what different teams need to see, when they need to see it. Using platforms like Looker, Power BI, Tableau, or custom UI when required, we design reporting tools that support real-time monitoring, goal tracking, and business reviews without manual prep. The goal is clarity, not complexity. Because the best dashboards don’t just look good, they get used.

Once your data is clean, we help you go deeper: predicting what’s likely to happen next and optimizing based on it. Whether that’s demand forecasting, churn risk modeling, intelligent routing, or personalized experiences, we build and deploy models aligned to your business goals. We use modern MLOps practices to ensure models can be trained, monitored, and improved over time, not abandoned after the pilot. And we don’t just ship models. We integrate them into your tools and workflows so the value shows up where people already work.

When LLMs can’t access your internal knowledge, they hallucinate. With RAG, we solve that. We design systems that combine semantic search with generative AI, allowing large language models to pull from your internal data securely, in real time. That makes your support bots smarter, your documentation dynamic, and your search tools way more useful. Use cases? Internal copilots. Document Q&A. Enterprise search that doesn’t rely on guesswork.

Agentic AI goes beyond chat. It enables intelligent systems to reason, plan, and act on their own. We help teams design and deploy goal-driven agents that connect to your internal systems (ticketing tools, databases, and APIs) and take multi-step actions. Think automated support triage, report generation, or back-office workflows that run themselves. We apply safe orchestration patterns and keep humans in the loop where needed, so autonomy doesn’t mean risk.

Connecting LLMs to enterprise systems isn’t just about access; it’s about control and context. We implement Model context protocol (MCP) to give models structured ways to query, update, and interact with your tools securely. That means LLMs that can check calendars, pull records, and update documents all within a policy with full traceability. It turns your data into a living, usable system for AI. Not a one-way firehose.