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.

Legacy → Cloud-native

Application modernization — incremental, safe, measurable.

Strangler-fig migrations, monolith decomposition, and replatforming. We modernize without big-bang rewrites — your business keeps running.

Talk to an expert

Modernization patterns

Move off legacy without rewriting from scratch.

Strangler-fig

Carve new services out of the monolith one bounded context at a time.

Microservices

Domain-driven decomposition with API gateways and shared platform tooling.

Replatform / refactor

Lift-and-shift to cloud, then refactor — pragmatic, not religious.

Database modernization

Oracle → PostgreSQL, mainframe → Postgres, schema-per-tenant migrations.

Containerization

Docker + Kubernetes, ECS, GKE — with day-2 ops included.

Cutover safety

Feature flags, shadow traffic, dark launches — modernize without outages.

Tech Stack

Stack we use

Kubernetes Docker Istio Argo CD Spring Boot Quarkus PostgreSQL Kafka Camunda LaunchDarkly Backstage Terraform

FAQs