Conversational AI features embedded in your application: customer support assistants grounded in your product documentation, onboarding guides that answer questions about your specific setup, knowledge base Q&A for internal teams, and company assistants that know your policies, pricing, and procedures. RAG pipeline connects the model to your knowledge base: documents indexed into pgvector or Pinecone, semantic retrieval at query time, retrieved passages injected into the model context so responses cite your actual content rather than general training knowledge. Context window management using tiktoken for accurate token counting: conversation history trimmed by removing the oldest turns when approaching the limit, or compressed via a rolling summary to preserve long-session continuity. Streaming responses via Server-Sent Events so users see output incrementally -- typical improvement from 4-8 second wait to sub-1-second time to first token. Confidence-based escalation: system prompt instructs the model to respond with a structured low-confidence signal when the query falls outside the knowledge base scope, triggering a handoff to a human agent rather than a hallucinated answer.

AI features that work on documents throughout their lifecycle. PDF extraction pipeline using pdfminer or PyMuPDF for structured text extraction that preserves section headings and table data -- not just raw character dumps that lose document structure. Word document processing via python-docx for formatted business documents. Chunking strategy calibrated to the task: summarisation needs large chunks to preserve narrative coherence; extraction needs smaller chunks with overlapping windows to prevent target fields from splitting across chunk boundaries. Documents longer than the model context window processed with a map-reduce approach: summarise or extract in parallel chunks, then combine outputs in a second pass. Specific use cases built and tested: contract clause extraction with function calling returning structured JSON per clause type, financial report summarisation with table data preserved, technical documentation translation into plain language for non-specialist audiences, and first-draft generation from structured data inputs (CRM records, form submissions) with your brand voice baked into the system prompt.

Adding AI capabilities to existing SaaS products -- not as a chatbot bolted onto the side but as features integrated into your existing product UI and data model. AI writing assistance that has access to the user's existing content and preserves their established style. Smart autocomplete calibrated to your domain vocabulary using few-shot examples in the system prompt rather than relying on generic GPT output. Content generation from structured data (CRM fields → sales email draft, property listing data → marketing copy, job spec → interview question set). AI-powered record categorisation and tagging that runs as a background job against your existing data. Multi-tenant prompt isolation: system prompts scoped per tenant with their brand voice, terminology, and constraints -- so two different customers using the same feature get outputs that feel right for their product. Per-user token usage tracking for cost attribution and usage-based billing. Streaming responses via SSE for AI features where users expect real-time output. Fallback handling for OpenAI API downtime: queued retry for non-real-time features, graceful degradation UI for real-time features.

Using OpenAI function calling with strict: true JSON Schema enforcement to extract typed, validated data from unstructured inputs -- reliably, not just when the input is clean and well-formed. strict: true mode guarantees the model outputs only the fields defined in the schema and never adds invented fields -- a critical difference for production use where unexpected output structure causes downstream write failures. Schema design matches your database model: nullable fields for optional data, enum constraints for categorical values, nested objects for related entities. Zod on the TypeScript side and Pydantic on the Python side validate the returned JSON before any write operation -- if the model returns a field value outside the permitted range, the record is flagged for review rather than written silently. Few-shot examples in the system prompt for domain-specific extraction tasks where the model's default interpretation differs from your field semantics. Accuracy benchmarking against a labelled test set before deployment to production -- we establish a precision/recall baseline before the feature goes live, so you know what error rate to expect.

Agents that use OpenAI function calling to complete multi-step tasks autonomously -- not a single API call but a loop where the model decides which tool to use next based on the results of the previous call. Tool definitions with JSON Schema describe the available actions: query an order, check inventory levels, apply a promotional code, send a confirmation email, create a support ticket. LangGraph for agents with conditional branches and state that must persist across multiple tool calls: an agent handling a complex customer escalation retrieves the order, checks the refund policy, assesses eligibility, drafts a response -- and if eligibility is borderline, routes to a human review step with all the context already compiled. Error handling for tool failures at each step: rate limit errors trigger exponential backoff retry; hard failures (order not found, inventory API down) surface a structured error to the model so it can respond appropriately rather than hallucinating a result. Token budget management across the tool use loop to prevent runaway agents from exhausting context or spend limits. See our multi-agent systems page for complex multi-agent orchestration.

Reducing OpenAI API costs for existing integrations without degrading quality -- typically achieving 30-60% cost reduction through a combination of model routing, caching, and prompt engineering. Model routing by task complexity: GPT-4o mini at approximately $0.15/1M input tokens for high-volume, focused tasks (classification, extraction, short-form generation) that don't need GPT-4o's reasoning depth; GPT-4o reserved for complex multi-step reasoning tasks where the quality gap is measurable. Semantic response caching with a vector similarity threshold: queries with cosine similarity above 0.95 against a cached query return the cached response rather than triggering a new API call -- effective for FAQ-style integrations where users ask the same questions with minor wording variations. OpenAI Prompt Caching for long system prompts that appear in every request -- repeated prompt prefix segments cached at the OpenAI side reduce both latency and cost. Prompt compression to reduce input token count without information loss. Output length constraints via max_tokens per task type. Production monitoring via LangSmith or a custom logging layer: cost per conversation, median and p95 latency, error rate by error type, and token usage by model -- so cost anomalies are visible in real time, not discovered at month-end billing.