Generative AISolutions

The honest answer is that it depends on your use case, your data, your latency budget, your evaluation criteria and your operational maturity - and any partner who tells you to fine-tune everything (or never fine-tune) is selling a preference, not engineering judgment. As a rough framework: prompt-engineering frontier models is the right default for most use cases - it's fast to ship, easy to iterate and benefits automatically from model provider improvements; the ceiling is what the base model can do with context. RAG is the right approach when your product needs to ground responses in your specific data (documentation, customer records, knowledge bases) and citation accuracy matters - but RAG quality lives or dies on retrieval quality, not generation quality. Fine-tuning is the right approach when your task has consistent structure (a specific output format, a specific judgment style, a specific tone) and you have evaluation data showing the base model can't reach the quality bar through prompting alone - but fine-tuning requires sustained operational investment in evaluation, retraining cycles and infrastructure. Most production generative AI products end up using two or three of these approaches together. We document the trade-offs for your specific use case during Phase 1 - including rejected alternatives - so the architecture decision is auditable, not vibes-based. If you're already committed to one approach because of internal constraints, we work within that constraint and flag the limitations honestly.

We treat these as measurable product requirements, not binary promises. Retrieval design, citation formatting, abstention policies and faithfulness metrics are built into the evaluation harness - with regression alerts when retrieval or model behaviour shifts. We document known failure modes and human-in-the-loop paths where your policy requires them, especially in regulated contexts where outputs augment rather than replace professional judgment.

We work across Anthropic, OpenAI, Google and self-hosted open-weight stacks - chosen against your latency, cost, compliance and capability bar. The evaluation harness stays constant so provider or model changes are measurable rather than guesswork.

Yes. We engineer multimodal pipelines with modality-specific model selection, routing, safety layers and unified observability - including streaming speech interfaces, image and video generation infrastructure with moderation and attribution hooks and combined text-document-media flows where your UX requires them.

Pre- and post-generation moderation, abuse detection, policy-driven refusals, attribution where outputs derive from third-party content and human-review queues are scoped to your audience and regulatory context. Residual risk is documented; we do not position moderation as infallible against a motivated adversary.

Yes - supervised, preference and programme-style fine-tuning where your evaluation data supports it. We only recommend fine-tuning when the harness shows a durable lift on your tasks versus strong prompting and RAG baselines, because fine-tuning adds operational surface area (retraining, eval gates, rollbacks).

Golden datasets from real user queries, automated eval in CI, online metrics (latency, refusal patterns, structured-output validity, citation checks where applicable) and A/B or shadow traffic when rollout risk warrants it. Model, prompt and retrieval changes ship through the same gates so quality regressions surface as engineering signals, not social-media surprises.

Yes - time-to-first-token, progressive rendering, optimistic UI, cancellation and partial structured-output reconciliation are standard parts of our frontend and API design for generative products.

Discovery through launch commonly spans roughly 12-26 weeks depending on multimodal scope, eval rigour, content-safety depth and integration breadth. Core team is typically a lead LLM/product engineer, a full-stack or AI-frontend engineer and an evaluation owner - scaled with workload.

Product brief, target users, representative queries and content samples, success metrics, latency and cost budgets, content-safety constraints, integrations, compliance context and target launch window. We respond within one business day with a scoped recommendation.