AI Development Services

AI implementation costs are highly variable and depend heavily on scope, data readiness, model complexity, and integration depth. As a working range, simple MVPs typically start around $10,000–$30,000, mid-complexity production systems land in the $75,000–$200,000 range, and large, custom, full-scale solutions can exceed $300,000+. A standalone discovery phase or Proof of Concept (PoC) usually runs $5,000–$10,000 before any production build begins.

Talent costs (ML engineers, data engineers, MLOps) often account for 40–50% of the budget, with the remainder split across data preparation, infrastructure, third-party model or API usage, and ongoing evaluation. We always provide a written estimate after a short scoping call so you see the assumptions behind the number, not just the number.

Timelines depend on data readiness and the level of integration required, so we share a realistic range up front rather than a fixed promise. As a guideline:

• Discovery / PoC: 2–6 weeks
• MVP in production: 8–16 weeks
• Full-scale, multi-system AI solution: 4–9+ months, often delivered in iterative releases
• Projects move faster when source data is already clean, labeled, and accessible, and slower when data engineering, compliance reviews, or change-management work is part of the scope. We re-baseline the timeline at the end of discovery once unknowns are resolved.

We run a five-stage process designed to de-risk decisions early:

• Discovery & feasibility — business goal, success metrics, data audit, build-vs-buy review.
• Proof of Concept — narrow-scope model on real (or representative) data to validate accuracy, cost, and latency before scaling.
• Engineering & MLOps — production model, data pipelines, evaluation harness, monitoring, CI/CD for models.
• Integration & UAT — wiring the model into your applications, workflows, and access controls; user acceptance testing.
• Launch & continuous improvement — staged rollout, drift monitoring, retraining cadence, and a documented runbook.

Each stage has a clear exit gate so you can pause, pivot, or stop with no surprise costs.

Security is built into the engagement, not bolted on. Standard practice on our AI projects includes signed NDAs and DPAs before data is shared, role-based access controls, encryption in transit and at rest, segregated client environments, and audit logging. Wherever possible we work inside your cloud tenancy (AWS, Azure, GCP) so sensitive data never leaves your perimeter.

For regulated workloads we align to relevant frameworks — common ones include GDPR, HIPAA, SOC 2, and ISO 27001 controls — and we’ll confirm specifics in scoping. We also document model-level risks (prompt injection, PII leakage, hallucination) and the mitigations applied, so your security and compliance teams have a clear artifact to review.

Yes — most of our work is integration-heavy rather than greenfield. We commonly connect models to CRMs (Salesforce, HubSpot), ERPs, data warehouses (Snowflake, BigQuery, Redshift), customer support tools, internal apps, and custom databases via APIs, event streams, or direct SDKs.

We’re model- and cloud-agnostic. Depending on what fits your constraints, we work with OpenAI, Anthropic, Google, Azure OpenAI, AWS Bedrock, and open-source options (Llama, Mistral) deployed on your infrastructure. The integration approach — synchronous API, batch pipeline, or embedded agent — is decided in discovery based on latency, cost, and data-sensitivity requirements.

Hand-off is treated as a deliverable, not an afterthought. At the end of the engagement your team receives a complete package: source code in your repositories, model artifacts and weights, data pipeline and infrastructure-as-code, evaluation datasets and test harnesses, a runbook covering deployment and monitoring, and an architecture decision record explaining the “why” behind key choices.

We also run structured knowledge-transfer sessions with your engineering, data, and product stakeholders, and remain available for a defined warranty period (typically 30–60 days) for fixes and clarifications. If you’d rather we stay on for managed support or continued iteration, we offer that as a separate engagement — but it’s optional.

We offer three common models so you can match the contract to the certainty of the scope:

• Fixed-scope, fixed-price — best for well-defined MVPs and discrete deliverables.
• Time & materials — best for evolving scope, R&D-heavy work, or rapid iteration.
• Dedicated AI pod — a cross-functional team (ML, data, MLOps, PM) embedded with you on a monthly basis for sustained roadmap delivery.

Most clients start with a fixed-price discovery or PoC and then move to T&M or a dedicated pod once direction is validated.

We also run structured knowledge-transfer sessions with your engineering, data, and product stakeholders, and remain available for a defined warranty period (typically 30–60 days) for fixes and clarifications. If you’d rather we stay on for managed support or continued iteration, we offer that as a separate engagement — but it’s optional.

Yes. AI systems behave differently from traditional software — model performance can drift as data and user behavior change, so post-launch support matters. We offer optional managed services covering monitoring (accuracy, latency, cost, drift), scheduled retraining, prompt and pipeline updates, incident response, and quarterly model reviews against your KPIs.

Support tiers range from a lightweight monitoring + on-call retainer to a fully managed pod handling continuous improvement. You can also self-manage using the runbook and tooling delivered at hand-off — the choice is yours.

• Fixed-scope, fixed-price — best for well-defined MVPs and discrete deliverables.
• Time & materials — best for evolving scope, R&D-heavy work, or rapid iteration.
• Dedicated AI pod — a cross-functional team (ML, data, MLOps, PM) embedded with you on a monthly basis for sustained roadmap delivery.

Most clients start with a fixed-price discovery or PoC and then move to T&M or a dedicated pod once direction is validated.

We also run structured knowledge-transfer sessions with your engineering, data, and product stakeholders, and remain available for a defined warranty period (typically 30–60 days) for fixes and clarifications. If you’d rather we stay on for managed support or continued iteration, we offer that as a separate engagement — but it’s optional.