AI Development & Testing Services

We are model-agnostic. We have production experience with OpenAI GPT-4o and o-series, Anthropic Claude (Sonnet, Opus, Haiku), Google Gemini, Meta Llama, Mistral, and open-source models via Hugging Face. Frameworks: LangChain, LlamaIndex, LangGraph, CrewAI, AutoGen, Semantic Kernel, and Azure AI Foundry. Vector stores: Pinecone, Weaviate, Chroma, pgvector, Azure AI Search. Evaluation: OpenAI Evals, Ragas, DeepEval, Promptfoo. Observability: LangSmith, Langfuse, Arize, Helicone, WhyLabs. We pick the stack based on your cost, latency, data residency, and compliance constraints — not a preferred vendor.

Traditional assertion-based testing does not work for LLMs — the same prompt can produce different valid answers. We use evaluation harnesses that measure distributions, not exact matches: groundedness scoring (is the answer supported by retrieved context?), correctness against gold datasets, semantic similarity, toxicity and bias detection, and LLM-as-judge with human validation. Every metric has a threshold that gates releases in CI. We also run adversarial tests (prompt injection, jailbreak) and regression suites on every prompt or model change — the same way traditional regression testing guards against code changes.

We build compliance in from day one. We map your AI use cases against the EU AI Act risk tiers, NIST AI Risk Management Framework, ISO/IEC 42001, and sector-specific rules (HIPAA for healthcare AI, GLBA and SR 11-7 for financial AI). We implement guardrails (input filtering, output moderation, PII redaction, policy enforcement), bias testing across demographic slices, audit logging of every prompt and completion, and documentation packages (model cards, risk assessments, data lineage) that auditors and regulators will accept. We have helped clients pass SOC 2 Type II, HIPAA, and internal AI governance reviews for AI features.

Yes — this is one of the highest-ROI pieces of an AI engagement. Typical levers: smart model routing (use cheap models for easy queries, expensive ones only when needed), prompt compression and caching, retrieval optimization (shorter contexts = lower tokens), structured outputs to avoid retries, and batching. Combined with cost-aware observability (per-tenant, per-feature, per-model dashboards), we typically cut LLM spend 30-60% without quality loss. One client reduced monthly OpenAI spend from $180K to $72K while improving CSAT.

Realistic timelines: a measurable prototype in 4-6 weeks, production pilot with guardrails and evaluation in 10-14 weeks, and scaled rollout with SLAs in roughly 4-6 months depending on integration complexity and compliance scope. We front-load the evaluation harness so you know from week 4 whether the accuracy target is achievable on your data — avoiding the classic AI project that looks great in demos and stalls in review. Engagements can run shorter for POCs or longer for regulated industries requiring formal validation.

Yes. Standalone AI testing engagements are a significant part of what we do. We onboard with your existing system, inventory the use cases and risks, build an evaluation dataset, set up continuous evaluation and adversarial testing, and deliver a quality and safety report with prioritized remediation. Many clients then convert to an ongoing AI QA managed service so every model upgrade, prompt change, and data refresh gets validated automatically. We often find issues production telemetry missed — silent drift, bias against specific user segments, or jailbreaks that bypass guardrails.