# Generative AI Development Services Build generative AI features into your product: content generation, enrichment, and drafting with output validation, evaluation, and cost controls. Canonical: https://www.metaborong.com/services/ai/generative-ai-development Service: ai/generative-ai-development ## Overview Generative AI development is the engineering of product features that generate text, structured content, and media with foundation models. We build the feature, not a demo - prompt and retrieval design, output validation, streaming, and evaluation that keeps generations on-spec. Work covers content generation, enrichment, summarisation, and drafting, grounded in your data and brand rules. Senior engineers own the build, India + global delivery. ## What is it? Generative AI development is the engineering of product features that generate text, structured content, or media using foundation models. Metaborong builds the full feature: prompt and retrieval design, output validation, streaming, evaluation, and cost controls. Work covers content generation, enrichment, summarisation, and drafting, grounded in your data. Senior engineers own the build, delivered from India with global reach. ## What we deliver - Generative feature shipped into your product, streaming and production-ready - Prompt and retrieval design with versioning and regression tests - Output validation and schema enforcement on every generation - Evaluation harness scoring quality, safety, and on-spec adherence - Cost controls: per-tenant ceilings, caching, and model routing ## Key concepts **Foundation model**: A foundation model is a large language or multimodal model, such as GPT, Claude, or an open-weights model, pretrained on broad data and adapted to specific tasks through prompting, retrieval, or fine-tuning rather than trained from scratch per use case. **Retrieval grounding**: Retrieval grounding fetches relevant source data at generation time and supplies it to the model, so outputs reflect and cite your proprietary content instead of relying on the model's training memory. It is what keeps factual generations accurate. **Structured output**: Structured output is generation constrained to a defined schema, such as JSON, so results parse reliably into downstream systems. Schema enforcement rejects or repairs malformed generations before they reach a user or another service. **Evaluation harness**: An evaluation harness is a labelled test suite that scores generation quality, safety, and on-spec adherence automatically, run in CI so quality regressions block deployment rather than surfacing in front of users in production. ## How we work 1. **Use-case and prompt design** We define the generation task precisely: inputs, desired outputs, tone, and the failure modes that matter. Prompts and retrieval are designed and versioned, with a labelled test set built before any feature ships. The output contract is fixed early so downstream systems can depend on it. 2. **Generation and grounding** We build the generation pipeline: model selection, retrieval grounding where facts matter, and structured-output enforcement so results parse reliably. Content generation and enrichment run against your data and brand rules, not generic prompts. Streaming and partial-result handling are engineered into the product surface, not bolted on after. 3. **Validation and evaluation** Every generation passes validation: schema checks, safety filters, and policy rules that sit in code, not prompts. A labelled evaluation harness scores quality and on-spec adherence, and regressions block deployment. Human review hooks fire where stakes are high, so nothing reaches a user unchecked. 4. **Rollout and cost control** The feature rolls out behind flags with per-tenant cost ceilings, caching, and model routing tuned to workload. Generation cost and quality are tracked in production. We hand over with a runbook and the evaluation set, so your team extends prompts and models without introducing regressions. ## Tech stack OpenAI (Models), Anthropic (Models), Hugging Face (Open-weights), LangChain (Orchestration), pgvector (Retrieval), Zod (Output schema), Redis (Caching), Sentry (Observability) ## When this fits ### Fits when - You want a generative feature inside an existing product, not a standalone demo. - You need outputs that parse reliably and stay on-spec, not freeform text. - You have brand rules or proprietary data that generations must respect. ### Does not fit when - You want a single chat assistant - that is a copilot or conversational agent. - You need autonomous multi-step task execution - that is AI agent development. - You expect novel model training - we integrate existing foundation models. ## FAQ ### What is generative AI development? Generative AI development is building product features that produce content with foundation models: text, structured data, summaries, or media. At Metaborong it means the full engineering job, not a prompt in a sandbox: retrieval grounding, output validation, streaming, evaluation, and cost controls, shipped into your product so the feature is reliable enough to put in front of users. ### How is this different from using ChatGPT directly? ChatGPT is a product; generative AI development builds the capability into yours. We control the prompts, ground outputs in your data, enforce output schemas, and run evaluations so results stay on-spec at scale. The model is one part: the validation, retrieval, and cost engineering around it are what make a generation feature production-safe. ### How do you stop the model producing wrong or off-brand output? Validation sits in code, not prompts. Outputs pass schema enforcement, safety filters, and brand-rule checks before they reach a user, and a labelled evaluation harness scores quality so regressions block deployment. Where stakes are high, a human review hook fires. Retrieval grounding keeps factual generations tied to your data, not model memory. ### Which models do you build on? OpenAI, Anthropic, Google, and open-weights via Hugging Face or self-hosted inference. We route per task: different models for drafting, structured extraction, and long-form generation, with fallback paths between providers for resilience and cost. Model choice is a workload decision made during architecture, not a default applied everywhere.