AI · Generative AI Solutions
Generative AI Development
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.
In short
What is Generative AI Development?
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
Concrete artefacts, not capabilities
- 01
Generative feature shipped into your product, streaming and production-ready
- 02
Prompt and retrieval design with versioning and regression tests
- 03
Output validation and schema enforcement on every generation
- 04
Evaluation harness scoring quality, safety, and on-spec adherence
- 05
Cost controls: per-tenant ceilings, caching, and model routing
Key concepts
Key terms, defined
- 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
Engagement phases
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.
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.
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.
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
What we build on
- OpenAIModels
- AnthropicModels
- Hugging FaceOpen-weights
- LangChainOrchestration
- pgvectorRetrieval
- ZodOutput schema
- RedisCaching
- SentryObservability
- OpenAIModels
- AnthropicModels
- Hugging FaceOpen-weights
- LangChainOrchestration
- pgvectorRetrieval
- ZodOutput schema
- RedisCaching
- SentryObservability
Scope
When this fits and when it doesn't
| This fits when | This doesn't fit when |
|---|---|
| You want a generative feature inside an existing product, not a standalone demo. | You want a single chat assistant - that is a copilot or conversational agent. |
| You need outputs that parse reliably and stay on-spec, not freeform text. | You need autonomous multi-step task execution - that is AI agent development. |
| You have brand rules or proprietary data that generations must respect. | You expect novel model training - we integrate existing foundation models. |
Related services
Adjacent engagements
- AI
AI Copilots & Internal Tools
Custom copilots for support, sales, and ops teams, grounded in your data and wired into your stack.
- AI
GenAI APIs & Backend Integration
Architect, integrate, and harden LLMs in your stack: auth, routing, fallback, cost controls, observability.
- AI
AI Knowledge Base
A compounding, LLM-maintained knowledge base your teams and agents query in seconds.
Frequently asked questions
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.
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.
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.
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.
Last reviewed · Reviewed by Metaborong engineering team
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