We build production-grade AI systems for healthcare, fintech, logistics, and enterprise operations — with named engineers in the contract, model-agnostic architecture, and compliance designed in from Sprint 1. Not POCs that die in slide decks. Engineering practice that lands AI in the P&L.
Trusted by Global Enterprises and Category Leaders
Most enterprise AI projects fail before production because the architectural decisions that determine success are made in Sprint 1 — not at launch. The Foundry's job is to get Sprint 1 right. Every engagement is scoped against a production go-live date, staffed with named engineers, and built on a model-agnostic stack that survives provider and pricing changes without rebuilds. Compliance is designed in from day one, not retrofitted before launch.
Talk to a Foundry EngineerAll delivered to production — each anchored in a reference architecture refined across QSS engagements in healthcare, fintech, logistics, and enterprise operations.
LLM-powered applications: document understanding, RAG systems, knowledge-base assistants, customer support automation, and domain-specific copilots. Built on multi-provider model abstraction with structured output validation and prompt versioning.
Multi-step agentic systems that plan, decide, and execute across enterprise workflows. Built on LangGraph, LangChain, and OpenAI/Anthropic tool-use frameworks for document drafting, decision support, and process automation in regulated industries.
Production computer vision for healthcare imaging (DICOM/PACS integration, anomaly detection), document understanding, defect detection, and visual search. PyTorch and TensorFlow stacks with full HIPAA-aligned audit trails.
Structured ML for demand forecasting, fraud and risk scoring, churn prediction, and operational forecasting. Sub-100 ms real-time inference. CDC-native data pipelines from production source systems. MLflow-managed lifecycle.
Domain-specific fine-tuning of open-weight models (Llama, Mistral, Qwen) and managed fine-tuning on OpenAI/Anthropic platforms. Used when off-the-shelf models do not meet accuracy, cost, latency, or data-sovereignty requirements.
HIPAA-aligned audit controls, SOC 2 Type II processes, ISO 27001 alignment, EU AI Act Article 12 logging — designed into the architecture in Sprint 1, not retrofitted before launch. Governance documentation is a build-time deliverable.
A five-stage methodology refined across enterprise AI engagements. Every stage produces concrete artifacts — not just status updates.
Weeks 1–2 · One painful, measurable business problem. Workshops with operating team, data audit, integration discovery, ROI quantification. Output: written engagement scope and a go/no-go recommendation.
Weeks 2–4 · Three architectural decisions signed off before any model code is written: data architecture (CDC, not samples), integration depth (production endpoints, not mocks), observability (Langfuse / OpenTelemetry from day one).
Weeks 4–12+ · Two-week sprints with named engineers, weekly demos, a documented production milestone schedule. Model selection happens here — architecture defines the model, not the reverse.
Weeks 12–16 · Phased go-live with rollback procedures, compliance validation (HIPAA / SOC 2 / EU AI Act), load testing, structured user-adoption plan. Done = users actively using in production.
Ongoing · Six months minimum post-go-live: model monitoring, drift detection, quarterly retraining, incident response. Engagement close transfers all artifacts — code, weights, configs, docs, runbooks — to your team.
Four verticals where the Foundry has the deepest reference architectures and the most production engagements.
HIPAA-compliant AI for clinical workflows, diagnostic imaging, patient-facing applications, and revenue-cycle automation. DICOM/PACS imaging platforms, AI-driven anomaly detection in radiology, FHIR-integrated clinical decision support. BAA-eligible architecture and ePHI audit logging from Sprint 1.
Real-time fraud and risk scoring (sub-100 ms inference), credit decisioning, KYC and AML automation, regulatory reporting, and customer-experience LLM applications. SOC 2 Type II–aligned with full audit trails and explainability layers.
SKU-level demand forecasting, route optimization with ML exception handling, last-mile delivery intelligence, warehouse anomaly detection. Integrated with existing ERP and TMS systems via CDC pipelines.
Document AI for legal, procurement, and HR; agentic systems for multi-step process automation; LLM-powered internal knowledge bases; AI-augmented business process re-engineering. Integrates with existing CRM, ERP, and ITSM.
Differentiators that show up in the contract — not just the pitch deck.
The architects who scope your engagement are the engineers who build it. Names, roles, and allocation percentages appear in the contract. Replacement-notification clauses are contractual, not verbal.
Reference architectures abstract model providers at the orchestration layer. Production systems route between OpenAI, Anthropic, Google, AWS Bedrock, Azure OpenAI, and open-weight models — without rebuilds. We have executed real production model migrations.
Not pilot completion. Not UAT sign-off. Production deployment with users actively using the system. If we cannot commit to production within a defensible timeline, we decline the engagement.
HIPAA, SOC 2, ISO 27001, and EU AI Act Article 12 controls designed into the architecture in Sprint 1 — not retrofitted. Audit logging and governance documentation are deliverables generated during the build.
Built for the $50M–$5B revenue range. Not optimized for Fortune 100 engagements at top-tier consulting rates. The Foundry is structured for organizations where the 20%/80% AI cost rule actually matters.
Scoping ($15K–$40K, 2–4 wks), Build ($150K–$900K+, 10–24 wks), Operate & Transfer ($20K–$80K/mo), and full Build-Operate-Transfer ($400K–$1.5M+) — pick the model that matches your ownership timeline.
An AI Foundry is a structured engineering practice for building production-grade enterprise AI systems — distinguished from generic "AI services" by a defined delivery methodology, a model-agnostic technology stack, named engineering teams, and contractual commitment to production deployment rather than pilot completion. The QSS AI Foundry operates this model for mid-market enterprises in healthcare, fintech, logistics, and enterprise operations.
Three structural differences. Scope: the major consulting AI foundries are optimized for Fortune 100 engagements at consulting-firm rates; the QSS Foundry is built for the $50M–$5B mid-market range. Delivery model: the QSS Foundry commits to named engineers in the contract with contractual replacement-notification clauses — not pooled-resource staffing. Architectural posture: the QSS Foundry is model-agnostic by design, with production systems that route between multiple AI providers.
Pricing depends on scope, integration complexity, AI capability mix, and compliance requirements. Typical ranges: Scoping engagements $15K–$40K (2–4 weeks); Build engagements $150K–$900K+ (10–24 weeks); Operate & Transfer engagements $20K–$80K per month; BOT engagements $400K–$1.5M+ depending on team size and operational duration. A scoping call produces a defensible cost range for your specific use case within two weeks.
Most production-grade AI builds take 10–16 weeks from Sprint 1 to production go-live, plus 6 months of post-go-live stabilization. Compressed timelines (under 8 weeks) correlate strongly with the failure pattern that produces the industry's roughly 80–95% AI project failure rate. The Foundry is designed for the production-discipline timeline, not the compressed-pilot timeline.
Yes. Every healthcare engagement includes BAA-eligible architecture, ePHI audit logging from Sprint 1, role-based access control, and HIPAA-aligned data retention policies. For clinical decision-influencing systems, FDA SaMD considerations are part of the architecture review. The QSS Foundry has shipped production healthcare AI across diagnostic imaging, clinical workflow automation, and patient-experience applications.
Yes — by architecture, not by marketing. Our reference architectures abstract AI providers at the orchestration layer so production systems can route between OpenAI, Anthropic, Google, AWS Bedrock, Azure OpenAI, and open-weight models based on cost, latency, capability, or data-sovereignty constraints. We have executed production model migrations in client engagements — model-agnosticism is a tested engineering discipline, not a portability promise.
The client. Standard QSS engagements transfer full ownership of source code, model weights, training scripts, infrastructure configuration, and deployment pipelines to the client at engagement close. The Build-Operate-Transfer model is specifically designed for clients who want eventual in-house ownership of the entire AI capability — not licensing rights. Contractual IP and source-code transfer terms are negotiated before kickoff, not at handover.
Schedule a 30-minute Foundry scoping call. We will walk through your use case, the data landscape, the integration scope, and the compliance environment. Output: a written go/no-go recommendation and — if go — a proposed Scoping Engagement scope. The first call is a direct technical conversation, not a pitch.
A direct technical conversation about whether your AI roadmap can ship to production — and what the Sprint 1 Architecture Contract would look like for your specific use case. No pitch deck. No generic demo. No follow-up unless you ask.
