There is a clear product slot
AI should read, decide, write, summarize, search, or route at a specific point in the user workflow.
04 AI product integration
AI product integration for apps that need more than a model call.
Connect Claude, OpenAI, Gemini, and retrieval to your product. We connect OpenAI, Claude, Gemini, retrieval, tools, APIs, permissions, logs, and review queues inside existing products.
CitationsSource answers
ReviewHuman correction
Any formatDocument input
EvalsBefore/after tests
What we build
AI integrations that fit the product workflow.
Capabilities
Where AI can fit inside an existing product.
01
LLM routing across OpenAI, Claude, Gemini, and local model options
02
Tool calling and structured outputs connected to product actions
03
RAG pipelines with citations, metadata filters, and evaluation sets
04
API integrations across dashboards, portals, CRMs, and internal tools
05
Cost controls, caching, model selection, retries, and observability
06
Human review queues for sensitive outputs and uncertain answers
AI foundation
What keeps AI features useful after launch.
01
Workflow Slot
We choose exactly where the model should read, decide, write, or request review.
02
Data Boundary
Permissions, retrieval scope, source access, and tenant rules are defined before rollout.
03
Quality Loop
Evaluation examples, logs, fallbacks, and blocked actions keep output measurable.
Before we build
What we need before adding AI to an existing product.
01
User action
What the AI should help the user do: search, extract, summarize, draft, score, classify, route, or explain.
02
Data sources
The records, files, APIs, tickets, policies, product data, or customer history the AI can use.
03
Access rules
Which users, roles, accounts, or tenants can see each answer, source, field, or generated output.
04
Output format
What the product needs back: JSON, draft text, score, citation, recommendation, task, or database update.
05
Failure handling
What happens when the model is uncertain, sources disagree, data is missing, or a tool call fails.
06
Cost target
Expected volume, latency needs, quality bar, and monthly budget before choosing model routing.
Build choices
The control layer behind product AI.
01
The product action
We define what AI should do in the product: search, extract, summarize, classify, draft, score, route, or explain. That keeps the feature from becoming a generic chat box.
SearchExtractRoute
02
Model choice
Claude, OpenAI, Gemini, and local models each fit different jobs. We choose based on accuracy, privacy, latency, context size, and monthly cost.
ClaudeOpenAIGemini
03
Data access
The model should only see the records, files, users, and tenant data it is allowed to use. Permissions and source boundaries are part of the integration.
PermissionsMetadataTenant scope
04
Backend control
Production AI needs validation, structured outputs, workers, retries, caching, and rate limits. That lives in the backend, not in the prompt.
FastAPINode.jsWorkers
05
Quality checks
We add evaluation examples, logs, blocked actions, fallback paths, and review screens so the team can see when AI is wrong or uncertain.
EvalsLogsFallbacks
06
Cost control
Small AI features can become expensive at scale. We manage model routing, caching, token limits, and usage reporting before volume grows.
CachingRoutingUsage
Shipped system
What separates product AI from a prompt box.
We place AI inside the product layer with permissions, structured outputs, tool access, and review paths so users can act on results.
View service →Shipped system
What separates product AI from a prompt box.
01
Document intake that keeps pages, sections, and metadata intact
02
Search tuned for accurate answers, not just uploaded files
03
Retrieval that finds the right policy, clause, record, or evidence
Workflow signals
How to know an existing product is ready for AI.
Data boundaries are known
Permissions, tenant scope, source access, and sensitive fields need to be defined before rollout.
Output can be checked
Structured responses, citations, eval examples, logs, and fallbacks make quality visible.
Cost can be managed
Model routing, caching, token limits, retries, and monitoring prevent small features from becoming expensive.
Why us
Why teams bring us in for AI product integration.
01
Model selection based on task, privacy, latency, and cost
02
Tool calling connected to actual product actions
03
Retrieval and citations for grounded outputs
04
Review queues for sensitive or uncertain cases
05
Logs, evaluations, caching, and cost controls
06
Next.js, Node.js, FastAPI, and local model orchestration experience
Insights
Useful next reads for AI integration projects.
Questions
Questions, answered clearly.
Short answers for teams deciding where AI belongs inside an existing product or workflow.
Q1
Can you add AI into an existing app?
Yes. We can add AI to an existing dashboard, CRM, portal, internal tool, SaaS product, or backend workflow without rebuilding the full product.
Q2
How do you keep answers grounded?
We use retrieval, source citations, structured outputs, evaluation examples, prompt rules, and logs so answers can be checked against the data that produced them.
Q3
Can you control model cost?
Yes. We design routing, caching, model selection, token budgets, fallbacks, and monitoring so the feature does not become expensive at scale.
Next step
Put one AI product integration into production.
Send the product workflow, data sources, and user action. We will show the safest first integration.