Services
Explore how we make AI dependable in the real world. Your users need clarity, speed, and safety - not guesswork.
Browse the services below to see exactly how we improve quality, performance, and trust at every stage.
Model and Application Evaluation for Reliable LLM Delivery
We assess how your LLM behaves in the real world—scoring task accuracy, faithfulness to source material, safety exposure, robustness to adversarial prompts, and responsiveness under load—so every feature ships with known quality and cost. Using a blend of curated gold datasets, automated judgments, and selective human review, we surface precise failure modes, quantify their impact, and prioritise the fastest, lowest-risk fixes across routes and personas.
You’ll receive route-level scorecards, reproducible experiments, and clear release thresholds tied to business goals. We wire these measures into CI/CD and staging environments so standards hold steady as prompts evolve, tools change, and models are swapped, ensuring your organisation can move quickly without losing reliability.
We separate retrieval from generation to reveal exactly where answers go off track, quantifying context recall, precision, and sufficiency before evaluating groundedness, clarity, and hallucination rates. With that visibility, we tune chunking strategies, embedding families, and context windows, producing before/after diffs so quality gains are both measurable and durable rather than anecdotal.
Freshness SLAs and source-coverage reports prove responses are anchored to your content, while a “RAG debugger” highlights whether the miss originated in retrieval or generation. The outcome is higher grounded-answer rates at predictable latency and cost, with guidance on when to add sources, restructure documents, or change routing.
RAG Validation and Systematic Performance Tuning
Guardrails and Safety Assurance for Responsible AI
We design domain-aware guardrails that keep interactions safe without throttling usefulness, covering toxicity, hate/harassment, self-harm, sexual content, PII exposure, and policy adherence across locales and channels. Purpose-built red-team suites probe jailbreaks, prompt-injection paths, and tool-use exploits, while calibrated thresholds minimise false positives so genuine users aren’t blocked.
Policy is translated into behaviour through audit-ready mappings and reproducible exercises. Regression packs integrate with your pipelines and log-based canaries watch live traffic, giving you early warning on safety drift, transparent reporting for stakeholders, and a pragmatic balance between protection, brand tone, and user experience.
We engineer responsiveness and efficiency from the ground up - tracking latency, first-token time, concurrency ceilings, and token economics—then refining prompts, routing, caching, and model selection to meet your SLOs. Replay-based load exercises expose bottlenecks under realistic traffic mixes, while guardrails on rate limits prevent noisy-neighbour effects and collapse under bursty workloads.
Cost dashboards align spend with quality targets so trade-offs are explicit: when to compress prompts, switch models, or route by intent. Expect faster experiences, steadier throughput during peak periods, and budgets that stay predictable as usage grows and features multiply.
Latency, Cost, and Throughput Optimization for Scale
Multilingual Quality, ASR/TTS, and Voice Experience Assurance
We make AI feel natural across languages and channels by evaluating text and voice for real accents, code-switching, noise, and cultural nuance. For ASR/TTS, we benchmark intelligibility, timing, and prosody; for dialogue, we align phrasing with brand tone and ensure locale-specific compliance and safety rules are respected, especially in regulated contexts.
Continuous checks catch drift early—vocabulary shifts, acoustic environment changes, or model updates—so global users get clear, on-policy, and quick interactions. Whether they’re reading a reply, speaking to an agent-assist flow, or using IVR, the experience remains consistent, courteous, and effective.
We operationalise quality beyond launch with pipelines that run offline, canary, and in-production evaluations on real traffic, backed by privacy-safe log sampling and human-in-the-loop reviews where judgment matters. Automated alerts flag meaningful changes in accuracy, safety, latency, or cost, reducing time-to-detect and time-to-mitigate across teams.
Weekly digests and structured post-mortems turn incidents into durable improvements, while links to observability systems ensure on-call responders have context, not noise. The result is a living assurance layer that scales with your product, absorbing model changes and prompt drift while keeping standards steady release after release.