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.

Data Engineering

ETL & data pipelines — built to run unattended for years.

Batch and streaming pipelines with reliability targets. Airflow, dbt, Fivetran, Kafka, CDC — we pick the right tool and run it in production.

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Pipeline capabilities

Pipelines that survive scale and schema drift.

Batch orchestration

Airflow / Dagster / Prefect DAGs with retries, SLAs, and pager integration.

Streaming & CDC

Kafka, Debezium, Kinesis — sub-second propagation from OLTP to the warehouse.

Source ingestion

Fivetran, Airbyte, Stitch, plus custom Python/Spark for the awkward sources.

Data quality

Great Expectations, dbt tests, Elementary — bad rows surface before they land in dashboards.

Backfills & replays

Idempotent jobs, partitioned writes, deterministic backfills.

Observability

OpenLineage + Marquez + Monte Carlo so you see lineage and incidents in one place.

Tech Stack

Stack we use

Airflow Dagster Prefect dbt Spark Databricks Fivetran Airbyte Kafka Debezium Kinesis OpenLineage Great Expectations

FAQs