AI features that actually ship.
WH Studio helps startups and product teams ship practical AI features, assistants, and automation systems that deliver real operational value — not just demo excitement.
Applied AI, end to end.
LLM product integration
- OpenAI API integration
- Prompt systems
- Model routing
- Guardrails and fallbacks
AI chatbots and support automation
- Support automation
- Lead qualification
- Knowledge retrieval
- Multi-step workflows
Document intelligence
- PDF analysis
- Contract review
- Data extraction
- Search and summarization
Applied computer vision
- OCR workflows
- Visual classification
- Inspection tools
- Search experiences
Where AI creates leverage.
Support automation
Reduce response times and increase coverage with AI-assisted support systems.
Content workflows
Automate structured drafting, categorization, and editorial assistance for teams.
Operational intelligence
Extract insights from business data and unstructured documents faster.
Workflow acceleration
Use AI to streamline repetitive tasks and remove friction from delivery teams.
From use case to production.
Use case discovery
Identify highest-value workflows, define risk boundaries, and map outcomes.
Data & retrieval
Structure knowledge sources, retrieval approach, and evaluation criteria.
Model & prompt design
Choose providers, build prompts, and define fallback logic.
Product integration
Implement AI inside your product with memory, permissions, and observability.
Test & optimize
Evaluate accuracy, latency, safety, and cost — then iterate.
- Simple AI integration
- 1–2wk
- RAG / assistant systems
- 4–8wk
- Typical cost savings vs naive impl
- 40–60%
- Starting engagement size
- $5K+
What production-grade AI actually looks like.
Most "AI features" shipped in 2024 were a chat widget and a prompt. The teams that compounded a real advantage rebuilt specific workflows around models, evaluations, and feedback loops. That is the work we do at WH Studio — not demos, not chatbots, but AI features that survive contact with real users and a finance team reviewing the token bill.
Evaluation infrastructure before the second prompt
The single biggest difference between an AI feature that compounds value and one that quietly degrades is an eval harness. Without it, you ship a prompt that worked on five examples and discover six months later that quality regressed silently when a model provider shipped new weights. Every AI engagement we run starts with a golden dataset, automated scoring, and a CI gate — before the second prompt ships. See our deeper breakdown in the AI integration playbook.
Cost control as a first-class concern
By month six of production AI, your token bill is a real line item. We route by complexity (cheap models for 70–80% of traffic), cache aggressively, trim context windows, and use provider batch APIs for async workloads. The combined effect is typically 40–60% cheaper than the naive single-model implementation, with equal or better accuracy.
Build vs buy — in the right places
Use a vendor for the parts that are not your differentiation: observability, prompt registries, eval orchestration. Build the parts that are: your retrieval pipeline, your workflows, your human-in-the-loop UI. Most teams invert this and pay for it for years.
Explore More Services
Solution Hubs
AI development, answered.
Frequently Asked Questions
Ready to build with AI?
Let's map the right AI workflow for your product, your team, and your cost envelope before you ship.
Continue with a Solution Hub
Jump into the hub that matches what you're building — deep dives on services, case studies, pricing, and how we deliver.