Generative AI Development
AI features your users actually use
From AI copilots and chat interfaces to content generation and intelligent search — we embed generative AI into your product with the engineering rigor of any other production system.
10×
faster content workflows
<1s
first-token latency targets
40%
avg. inference cost savings
Outcomes that move the bottom line
We measure success in business results — revenue, cost, speed, and reliability — not lines of code shipped.
10×
Faster content cycles
Drafting, summarization, and media-generation pipelines that compress days of manual work into minutes.
+35%
Engagement lift
Copilots and semantic search keep users inside your product instead of bouncing to a search engine.
<1s
Instant responses
Streaming UX with first-token latency budgets so AI features feel immediate, never laggy.
Features that ship
We kill gimmicks early and invest in the AI capabilities your users actually adopt and return for.
What we deliver
AI copilots & assistants
In-product assistants with streaming UI, context awareness, and tool access — built on the Vercel AI SDK and modern model APIs.
Content & media generation
Text, image, and audio generation pipelines with brand controls, human review steps, and cost-efficient batching.
Semantic search & recommendations
Embedding-powered search and discovery that understands meaning, not just keywords — across products, docs, and media.
Document intelligence
Extraction, classification, and summarization over contracts, invoices, and reports — with structured outputs your systems can consume.
Specialized offerings
Your data, in the loop
RAG Pipelines
Retrieval-augmented features that ground generation in your private content — knowledge bases, product catalogs, and document stores.
- Vector store setup (Pinecone, pgvector, Qdrant)
- Smart chunking & metadata strategies
- Citation-backed answers users can verify
Production-grade, not demo-grade
LLM App Engineering
Prompt management, caching, fallbacks, and cost observability — the unglamorous engineering that makes AI features dependable.
- Prompt versioning & A/B testing
- Semantic caching to cut inference costs
- Multi-provider fallback & rate-limit handling
Specialized model behavior
Fine-Tuning
Custom-tuned models for brand voice, domain classification, and structured extraction where prompting hits its ceiling.
- Training data curation & quality control
- LoRA adapters for open-weight models
- Eval-driven before/after comparisons
Concrete deliverables
Every engagement ships tangible artifacts you own and can run without us — not slideware.
- AI feature roadmap and feasibility assessment
- Production LLM integration with streaming UX
- Prompt management, caching, and multi-provider fallback
- Semantic search / embeddings infrastructure
- Cost and quality observability dashboards
- A/B testing harness for prompts and models
Is this the right fit?
We do our best work when the fit is right. This practice shines for teams like these:
- SaaS products adding an AI copilot or assistant
- Content and marketing teams scaling their output
- Search and discovery that needs to understand meaning
- Document-heavy workflows needing extraction and summarization
Tools of the trade
Our process
01
Feature discovery
We identify the AI features with the strongest user value and feasibility — and kill the gimmicks early.
02
Model & UX prototyping
Rapid experiments across models and interaction patterns to find what feels right and performs well.
03
Production build
Streaming UX, error handling, cost controls, and evals — integrated into your existing stack and CI/CD.
04
Measure & iterate
Usage analytics and quality metrics drive continuous improvement after launch.
Ways to work together
Choose the model that matches your stage and risk profile — or tell us your constraints and we'll recommend one.
Defined outcome, fixed budget
Fixed-scope project
A scoped statement of work with milestones, acceptance criteria, and a target launch date. You know the cost and the deliverable before we start.
Best for: Well-defined builds with clear requirements and a firm timeline.
An embedded senior team
Dedicated squad
A cross-functional pod — engineering, design, and delivery — that works as an extension of your team with sustained velocity and full ownership.
Best for: Evolving roadmaps that need ongoing capacity and accountability.
Ongoing partnership
Retainer & support
Reserved monthly capacity for iteration, maintenance, performance work, and on-call reliability — with response-time SLAs that match your risk.
Best for: Maintenance, iteration, and reliability after launch.
Common questions
Model routing (small models for simple tasks), semantic caching, prompt compression, and batch processing. We instrument cost per feature from day one so there are no surprise bills.
Let's build something
Have a project in mind?
We'd love to hear about it. Tell us what you're building and we'll get back to you within 24 hours.