Generative AI

What is generative AI?

Generative AI is the class of models that produce new content — text, images, audio, video, or code — conditioned on a prompt. In production, generative AI is rarely just an API call; it is a pipeline that combines a foundation model with retrieval, structured output, evaluations, guardrails, and observability.

We build generative AI systems that survive contact with real users and real workloads. That means picking the right foundation model for the task, grounding outputs in your data, instrumenting quality, and managing the cost curve as volume grows.

Key terms used on this page:

Foundation model: A large model trained on broad data (GPT-4o, Claude Sonnet, Gemini, Llama, Mistral) that can be adapted to specific tasks via prompting, retrieval, or fine-tuning.
RAG (Retrieval-Augmented Generation): A pattern where the model is given relevant context from your data store at inference time so it answers from your sources, not its training data.
Prompt engineering: Designing the system prompt, examples, and structured output schema that turn a foundation model into a reliable component of a workflow.
Evaluation harness: A test suite of prompts and expected outputs that runs on every prompt or model change, measuring accuracy, hallucination rate, and regressions.
Guardrails: Pre- and post-generation filters (content policy, PII redaction, schema validation) that keep outputs safe and well-formed.
LoRA / Low-Rank Adaptation: A lightweight fine-tuning technique that adapts a model to your domain without retraining all its weights.

How does generative AI work in production?

A production generative AI feature typically has six layers. Skipping any of them is how teams ship demos that fail in week three:

1. Input handling — Validate and sanitize user input, redact PII before it ever reaches the model.

2. Retrieval — Pull the relevant context from your knowledge base, database, or document store. Without this layer, the model is guessing from its training data.

3. Prompting — A versioned system prompt with few-shot examples, role definition, and a strict output schema (usually JSON).

4. Generation — The model call itself. We almost always use streaming, with retries on transient failures and a fallback model for outages.

5. Post-processing — Schema validation, content moderation, citation checks, and any business-rule enforcement.

6. Observability — Token usage, latency, cost per request, hallucination rate, user feedback signals — all logged and dashboarded.

Most "ChatGPT integration" projects fail because teams build steps 3 and 4 only and treat the rest as polish. Production-grade generative AI is mostly the unglamorous infrastructure around the model call.

How does retrieval-augmented generation work?

RAG is the single most important pattern in applied generative AI. It is how you get a foundation model to answer from your data instead of its training data. The architecture has four moving parts:

1. Ingestion — Documents are chunked, embedded with a model like OpenAI text-embedding-3-large, Cohere embed-v3, or Voyage AI, and stored in a vector database (Pinecone, Weaviate, Qdrant, pgvector on Postgres).

2. Retrieval — At query time, the user's question is embedded and the closest chunks are pulled from the vector store. We almost always combine vector search with keyword (BM25) search and a reranker (Cohere Rerank, Voyage rerank-2) — pure vector retrieval underperforms in most enterprise corpora.

3. Generation — The retrieved chunks are inserted into the prompt with explicit instructions to cite sources and refuse to answer if the context is insufficient.

4. Evaluation — Faithfulness (does the answer match the sources?), relevance (did we retrieve the right chunks?), and answer quality (is it useful?) are scored automatically with frameworks like Ragas or our own evaluation harness.

For most knowledge-base, support, and internal-search use cases, a well-built RAG system outperforms fine-tuning at a fraction of the cost and is far easier to update as your data changes.

How do you build image and video generation pipelines?

Image and video generation has matured fast, but the failure mode is different from text. Models are now visually impressive but inconsistent across runs, brands, and characters. The pipelines we build address that:

Brand consistency — We train LoRAs (Stable Diffusion, FLUX) on your product, brand, or character assets so generations stay on-brand. For mascot or character continuity, we use IP-Adapter, ControlNet, and reference-image conditioning.
Composition control — ControlNet and regional prompting let designers specify layout, pose, and depth — not just hope the model gets it right.
Prompt templates — A library of versioned prompt templates per use case (product hero, lifestyle scene, social post) so a marketer doesn't need to be a prompt engineer.
Human review — A queue where a designer approves or rejects generations before publication. Fully autonomous brand image generation is still risky; human-in-the-loop is the realistic shape.
Video — For motion, we use Runway Gen-3, Luma Dream Machine, Kling, or Pika depending on style and length. Most production video pipelines today are short clips (3–10 seconds) stitched into longer narratives, not single end-to-end generations.

How do you build voice and audio generation?

Voice generation has crossed the uncanny-valley line for most use cases. We build voice features on:

ElevenLabs for cloned and custom voices in narration, IVR, and conversational agents — the quality bar in 2026.
OpenAI TTS / Realtime API for low-latency conversational voice (sub-500ms response).
Cartesia when latency matters more than voice fidelity.
PlayHT and Resemble AI for specific accent or language coverage.

The hard parts are not generation but the surrounding system: voice activity detection, interruption handling, turn-taking, and grounding the agent in your business logic. A voice that sounds great but books the wrong appointment is worse than a typed form.

How do you build code generation tools?

Code generation is the highest-leverage generative AI use case for most engineering teams. We build:

Internal copilots grounded in your codebase, style guide, and architecture docs — typically on Claude Sonnet or GPT-4o with a custom retrieval layer.
Code-review assistants that catch the patterns your team cares about (security, performance, naming) without drowning developers in noise.
Spec-to-code workflows for repetitive scaffolding — new API endpoints, database migrations, CRUD UIs — where the model handles 80% and a developer reviews.
Migration assistants for legacy code (jQuery to React, Python 2 to 3, monolith to microservices) — narrow, well-defined transformations where models are strong.

We do not recommend replacing GitHub Copilot or Cursor for general autocomplete. We recommend building specialized tools on top of them for the specific friction your team has.

Should you build, buy, or partner for generative AI?

The market is split between foundation-model providers (sell APIs), application vendors (sell finished features), and custom builders (build the layer in between). Here is how the trade-off plays out:

Option	Best for	Speed	Differentiation	Cost (per year)	Lock-in risk
Buy SaaS (Jasper, Copy.ai, Writer, Notion AI)	Generic content, marketing copy, no engineering capacity	Days	None — competitors get the same output	USD 5k–100k seats	High — vendor controls model and roadmap
Buy foundation API (OpenAI, Anthropic, Gemini, Cohere) and prompt-engineer it	Simple workflows, internal tools, MVP	2–6 weeks	Low — same model everyone else uses	Usage-based, USD 1k–50k+	Medium — model swap is possible but painful
Open-weight self-host (Llama 3, Mistral, Qwen, DeepSeek via Together.ai, Fireworks, AWS Bedrock)	Privacy-sensitive workloads, predictable cost at scale	4–12 weeks	Medium — you control the stack	Infrastructure + ops	Low
Partner-built custom pipeline (our model)	Differentiated workflows on your data, regulated industries, brand-critical content	6–14 weeks	High — your data, your prompts, your evals	Predictable, IP retained	Low — you own the code
Build in-house	Mature ML org with embedding, RAG, and evals expertise	4–9 months	Highest	Highest fully-loaded cost	Low

The pattern that works: buy the foundation model from one of the big three (OpenAI, Anthropic, Google), partner on the application layer (retrieval, prompts, evals, UI), self-host open-weight models only when privacy or unit economics demand it. Avoid buying full SaaS for anything that touches your differentiated workflows.

For voice specifically, ElevenLabs has the quality lead and is worth buying. For image, Stability AI / FLUX self-hosted gives you brand-LoRA control that the closed providers do not. For text, Anthropic and OpenAI are roughly interchangeable on most workloads — pick based on the specific evaluation results, not the brand.

How do you evaluate generative AI quality?

Most teams ship generative AI without evaluations and discover quality problems from angry users. We treat evaluation as a first-class part of the build:

Layer	What we measure	How
Unit	Does each prompt produce correct output on a golden set?	Pytest-style assertions with LLM-as-judge for fuzzy outputs
Faithfulness (RAG)	Does the answer match the retrieved sources?	Ragas, custom rubric scoring with Claude or GPT-4 as judge
Safety	Does the system refuse harmful requests and avoid PII leakage?	Red-team prompt suite, guardrail tests
Latency / cost	p50, p95, p99 latency and cost per request	OpenTelemetry, Langfuse, Helicone, or custom
User feedback	Thumbs up/down, edit-distance from final to AI output	Logged and reviewed weekly

Without these, "the model is good" is an opinion, not a fact. With them, you know exactly what changed when you swap models or update a prompt — which you will, often.

What does a generative AI engagement look like with us?

A typical first-feature engagement runs 8 to 14 weeks:

Weeks 1–2: Use-case definition, success metrics, model and vendor selection, golden-set construction.
Weeks 3–6: Build the pipeline — retrieval, prompts, post-processing, guardrails, observability.
Weeks 6–9: Evaluation harness, red-team testing, internal pilot with real users.
Weeks 9–12: Production launch with monitoring, on-call playbook, and cost dashboards.
Weeks 12–14: Iteration on the first weeks of real-user data — prompt tuning, retrieval tuning, latency wins.

Outcomes we hold ourselves to: a working production feature, an evaluation harness your team can run, a documented prompt library, and a cost model that projects unit economics at 10x and 100x volume.

After launch, most clients keep us on a smaller retainer (10–30 hours/month) for prompt tuning, model upgrades, and adding adjacent features. Foundation models update every few months and your prompts will drift; budgeting for ongoing tuning prevents quality regressions.

What does generative AI cost?

For a single production feature built end-to-end, expect USD 40,000 to USD 150,000 for the build, depending on complexity, retrieval scope, and integration count. Multi-feature platforms with shared infrastructure (vector DB, evaluation harness, prompt registry) run USD 150,000 to USD 400,000.

Inference costs at runtime depend on the model and volume:

Text (Claude Sonnet, GPT-4o): USD 0.002–0.05 per typical call
Text (open-weight via Together / Fireworks): USD 0.0005–0.01 per call
Image (Stable Diffusion, FLUX): USD 0.005–0.05 per generation on managed infra
Video (Runway, Luma, Kling): USD 0.10–1.00 per clip
Voice (ElevenLabs, OpenAI Realtime): USD 0.05–0.30 per minute

We always model unit economics before building. If a feature does not pencil out at projected volume, we say so before you spend the money.

For pricing on adjacent services, see our Pricing page.

Frequently asked questions about generative AI

Should we use OpenAI, Anthropic, or Google Gemini for our generative AI use case?

It depends on the workload. We default to Anthropic Claude for long-context reasoning, drafting, and tool use; OpenAI GPT-4o / o-series for general chat, multimodal, and ecosystem maturity; Google Gemini when you need 1M+ token context or are deep in Google Cloud. We routinely run evaluations across all three on your data before recommending one — vendor reputation is not a substitute for measured accuracy on your prompts.

How do we keep generative AI from hallucinating on our data?

Three layers: retrieval-augmented generation grounded in your authoritative sources, structured output with schema validation, and an evaluation harness that runs every prompt change against a golden set. Hallucination is never zero, but with grounding plus evals you can drive it down to a level where the output is genuinely usable in production.

Can we generate images and video for our brand at scale?

Yes. We build pipelines on Stability AI, Black Forest Labs (FLUX), Ideogram, or Midjourney for stills, and Runway, Luma, or Kling for video — with brand-locked LoRAs, prompt templates, and a human review step. Fully autonomous brand-safe image generation is still rare; the realistic shape is human-in-the-loop with AI doing 80% of the work.

How much does it cost to run a generative AI feature in production?

Per-call inference is usually USD 0.001 to USD 0.10 depending on model, context length, and output size. The bigger cost is engineering — building the retrieval, evaluation, monitoring, and guardrails. Expect USD 40,000 to USD 150,000 to ship the first production feature, then unit economics that scale predictably.

Can we keep our prompts and customer data private?

Yes. We deploy through enterprise endpoints (OpenAI Enterprise, Anthropic API with zero data retention, AWS Bedrock, Azure OpenAI, Vertex AI) where your data is not used for training and is not retained beyond what you configure. For the highest-sensitivity workloads we deploy open-weight models (Llama, Mistral, Qwen) inside your VPC.

Should we fine-tune a model or just use prompting and RAG?

Start with prompting plus retrieval. Fine-tune only when you have a stable, repeatable task with clear failure modes that prompting cannot solve — typically formatting consistency, brand voice, or domain-specific reasoning patterns. We see teams fine-tune too early about 80% of the time.

Will generative AI replace our writers, designers, or coders?

No, but it will compress the work. The teams that adopt it well treat generative AI as a force multiplier — one writer producing the volume of three, one designer iterating ten times faster, one engineer shipping more features per sprint. Headcount usually stays flat while throughput rises.