How long does an AI MVP usually take?

Most LLM-based MVPs land in 4–8 weeks. Computer vision or model-training projects typically run 8–14 weeks depending on data readiness. We commit to a delivery date after the 1-week discovery phase — and we hit it on 97% of projects.

Do we own the model and the code?

Yes — 100%. All source code, fine-tuned model weights, prompts, ML Ops infrastructure and documentation are transferred to you at delivery. We sign IP-assignment clauses upfront in the MSA.

Can the AI run inside our VPC / on-prem?

Absolutely. We routinely deploy on AWS, Azure and GCP private VPCs. For clients with strict data-residency or air-gap needs we support fully on-prem deployments using open-source models.

How do you handle hallucinations and accuracy?

Every project gets an evals harness: a golden dataset, automated regression tests on every model/prompt change, and production drift detection with alerting. We don’t release an LLM feature without measured accuracy on your data.

Which foundation model should we use?

It depends on the use case. We benchmark GPT-4, Claude, Gemini and open-source models against your task during the audit. Sometimes a smaller open-source model beats a frontier model at a fraction of the cost.

How do you ensure data privacy & compliance?

We’re ISO 27001 certified. We sign DPAs and BAAs upfront. We support HIPAA, GDPR, PCI-DSS and regional data-residency requirements.

What happens after launch?

Every Fixed Scope project includes a 3-month post-launch warranty. After that, optional support packages range from business hours to 24×7 SLA-backed. Most clients move to a Dedicated Team for ongoing iteration.

Can you work with our existing engineering team?

Yes. Staff Augmentation and Dedicated Team models are designed for it. Our engineers join your Slack, Jira and GitHub, pair-program with your team and hand off cleanly.

Warehouse · Lake · Pipelines

Data engineering — warehouses, lakes, and pipelines built to scale.

Modern data stack engineering: ingestion, transformation, warehousing, and BI delivery. Pick the right tools — we have shipped on Snowflake, Databricks, BigQuery, and Redshift.

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Capabilities

Across the entire data stack.

Data warehouse

Snowflake, Databricks SQL, BigQuery, Redshift — design, build, optimize.

Data lake / lakehouse

Delta Lake, Iceberg, Hudi — open table formats with ACID and time travel.

Pipelines

Airflow / Dagster / Prefect orchestration plus dbt for transformation.

Streaming

Kafka + Flink / Spark Structured Streaming for real-time data flows.

Data quality

Great Expectations, dbt tests, Elementary — quality gates on every pipeline.

Governance

Unity Catalog, Atlan, Collibra — lineage, ownership, and access control.

Tech Stack

Stack we use

Snowflake Databricks BigQuery Redshift dbt Airflow Dagster Prefect Kafka Spark Flink Delta Lake Iceberg Great Expectations Unity Catalog

FAQs