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.

GCP Partner

Google Cloud — built for data, ML, and Kubernetes.

GCP architecture, BigQuery data platforms, Vertex AI ML, and GKE infrastructure. We are GCP partners with cross-product depth.

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What we build

GCP from data to AI.

GKE & Anthos

Multi-cluster, multi-region GKE with Anthos Service Mesh and Config Management.

BigQuery platforms

BigQuery + Looker + Dataform — modern data warehouses without managing infra.

Vertex AI

Vertex AI training, Model Garden, and Vertex Pipelines for ML in production.

Cloud Run & Functions

Serverless containers + functions for event-driven and request-based workloads.

Security

VPC Service Controls, IAM Conditions, Workload Identity, Binary Authorization.

Observability

Cloud Operations Suite plus Datadog/Honeycomb for deep instrumentation.

Tech Stack

Stack we use

GKE Anthos BigQuery Dataflow Pub/Sub Cloud Run Cloud Functions Vertex AI Cloud SQL Cloud Spanner Firestore Looker Dataform

FAQs