Data platform and AI engineering
We make Databricks and Spark platforms cheaper to run and more reliable, with trustworthy AI agents on top. Fixed scope, fixed price.
Clients include Nike and Inter&Co.
A data platform and AI engineering firm
ISTech sells outcomes, not hours. Every engagement is a package with a fixed scope, a fixed price and a written deliverable. Work runs async by default: one kickoff call, one written update a week, one handover call. Founded in 2023.
Databricks cost and reliability audit
For: Teams on Databricks or Spark whose compute bill grows faster than their data, or whose jobs fail too often.
We review your 10 most expensive or least reliable jobs and deliver a ranked report with expected savings, effort, and a fixed price to implement the top fixes. The fee is credited toward the fixes if you sign within 30 days.
Analytics serving layer
For: Teams where every dashboard has its own aggregate table and the numbers disagree.
One multi-grain table with a documented metric catalog, data-quality checks on the build, and up to 3 dashboards migrated to it.
Analytics agent pilot
For: Teams that want people to ask data questions in plain language without trusting a chatbot blindly.
A natural-language to SQL agent on your warehouse for one domain, with confidence scoring against validated questions, low-confidence alerts, and tracing and feedback in MLflow. Optional MCP server so engineers query the same data from their IDE.
Pipeline automation sprint
For: A recurring report or process still done by hand in spreadsheets.
That process turned into an orchestrated pipeline on Airflow or Databricks Workflows, with data-quality checks and alerting.
Retainer
For: Clients who finished a package and want ongoing help.
A fixed monthly block of hours, used async for reviews, fixes and architecture questions. No on-call, no fixed meeting schedule.
Selected results
Work from our engagements. Clients include Nike and Inter&Co.
98%
Fewer data reads on a daily Spark job (Nike)
A daily job scanned multiple terabytes per run, mostly small event files it never used. We fixed partition and column pruning and reshaped the read path around how the job filters. It now reads 98% less data per run.
Weeks → hours
A/B test reporting, automated (Nike)
Every experiment readout was assembled by hand in spreadsheets. We built an Airflow pipeline that computes the metrics and statistical tests from declarative config and publishes the readout. Same method on every test, hours instead of weeks.
1 table
One multi-grain table instead of many aggregates (Nike)
Each dashboard had its own aggregate table and the numbers disagreed. One table built with Spark GROUPING SETS and ROLLUP now holds every grain the consumers need. Other teams adopted the pattern.
Lakehouse
Customer segmentation platform (Inter&Co)
A medallion lakehouse on S3 and Delta Lake with CDC ingestion through Kafka, data quality as code that aborts bad loads, and a near-real-time update path between batch cycles.
Agent
Production analytics agent (Nike)
A natural-language analytics agent running in production that scores its own confidence, plus an MCP server so engineers query the same data from their tools.
Fix the platform, then add AI
Data platforms tend to cost more and break more each quarter, and AI agents inherit every flaw underneath. We fix the platform first, then build agents your team can trust. Every package ends with production software and a runbook.
Book a 30-minute scoping call →Why choose ISTech?
Delivering for enterprise clients since 2023
Fixed scope and fixed price on every package. No hourly billing.
Hands-on with Databricks, Spark, Kafka, Airflow, dbt and Terraform
14+ professional certifications (Anthropic, AWS, Databricks, Azure)
Async delivery across 6+ timezones
Senior-level delivery on every engagement, one accountable lead
Book a 30-minute scoping call
Tell us what your platform costs, what keeps failing, or what you want AI to answer. We reply by email to set up the call.