QuantForge HQ builds production-grade AI features into existing enterprise products. LLM orchestration, prompt engineering, RAG systems, agent architecture, evaluation harnesses, and cost-aware deployment — engineered to ship, not demo.
The full engineering discipline of putting AI into production — not a single-model wrapper on top of an existing app.
Use-case scoping, model selection trade-offs, cost/latency modeling, success-criteria definition before any build.
Structured prompting, few-shot design, instruction hierarchies, version-controlled prompt library with evals.
Function calling, tool schemas, multi-step agent orchestration, agent-to-agent coordination patterns.
Embedding strategy, vector-store selection, retrieval evaluation, hybrid search, context-window management.
Golden-set eval pipelines, LLM-as-judge configurations, regression tracking per model + prompt version.
Input validation, output filtering, PII detection, injection defense, content-policy compliance, hallucination mitigation.
Model routing by task complexity, caching strategies, batch processing, streaming, observability on per-call economics.
Claude, GPT, Gemini, open-source models. Router layer that selects per-task. Fallback + failover strategies.
Per-prompt cost + latency dashboards, output-quality regression tracking, model-drift detection.
QuantForge's AI integrations are engineered for production economics and reliability. Every system ships with evals, guardrails, and cost dashboards.
The same five-step operating model we use for every engagement.
30 minutes. Audit of current state, problem definition, budget sizing.
Written proposal in 5 business days. Fixed management fee, scoped deliverables.
Credentials, tracking, account linking. Tenant-isolated environments.
50 specialists across 15 departments run the engagement under senior oversight.
Weekly reports, monthly strategic review, continuous optimization.
Every engagement handles sensitive client data. Our policies are explicit.
Client data used in AI integrations is handled per our Privacy Policy and Terms of Service. We do not use client data to train foundation models. Data is transmitted to third-party LLM providers only under appropriate data-processing terms (Anthropic, OpenAI, Google DPAs).
For engagements requiring data residency, we architect with region-pinned deployments, self-hosted open-source models, or enterprise-contracted model endpoints that guarantee non-training and data-residency terms. These decisions are documented before any production deployment.
Every AI integration ships with input validation, output filtering, and human-in-the-loop review where scope requires. PII detection and redaction is applied at both input and output boundaries. Injection defenses are implemented against known prompt-injection patterns.
For customer-facing AI features, escalation paths to human review are designed into the system from day one. High-risk decisions (legal, financial, medical) are not made autonomously by AI systems we build without explicit human approval gates.
Integrations comply with each model provider's usage policies (Anthropic Acceptable Use, OpenAI Usage Policies, Google AI Principles). We do not build systems for prohibited use cases (CSAM, weapons, deceptive political content, etc.).
Third-party API usage respects rate limits, backoff protocols, and fair-use patterns. Cost and usage monitoring alerts prevent runaway spend. Integration code is documented to allow client technical teams to audit or replace models without rewriting the application.
Tell us your current product, what AI use case you are exploring, and any data-residency constraints. Leadership reads every inquiry within 48 hours.
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