AHK.AI's OpenAI integration specialists connect GPT-4, embeddings, and Retrieval-Augmented Generation (RAG) to your applications with production-grade reliability. We handle prompt engineering, function calling, conversation memory, and cost optimization—deploying secure AI features that scale with your business.
What You'll Get
OpenAI API integration into your application
Prompt engineering and optimization
Function calling and tool use setup
Conversation memory implementation
RAG pipeline with vector database
Cost monitoring and optimization
Error handling and retry logic
Complete documentation and examples
How We Deliver This Service
Our consultant manages every step to ensure success:
GPT-4 Turbo offers the best price-performance for most use cases. GPT-4 is better for complex reasoning. GPT-3.5 Turbo is 10x cheaper for simple tasks. We analyze your use case and recommend the optimal model mix.
What is RAG and do I need it?
RAG (Retrieval-Augmented Generation) lets GPT answer from YOUR data—documents, FAQs, products. If you want AI that knows your business, RAG is essential. We set up the vector database, embeddings pipeline, and retrieval logic.
How do you handle prompt engineering?
We design prompts with clear instructions, examples (few-shot), and output formatting. We iterate based on real responses, optimize for quality and cost, and document the final prompts for your team to maintain.
What about function calling?
Function calling lets GPT trigger actions in your system—book appointments, query databases, send emails. We define your functions, handle the API logic, and build the execution layer for reliable automation.
How do you optimize OpenAI costs?
We implement: (1) Prompt compression to reduce tokens, (2) Caching for repeated queries, (3) Model routing (cheap models for simple tasks), (4) Streaming for faster perceived response, (5) Usage monitoring with alerts.
Is OpenAI API secure for enterprise use?
Yes. OpenAI doesn't train on API data. For maximum security, we can deploy via Azure OpenAI which offers data residency, private endpoints, and enterprise compliance (SOC 2, HIPAA). We handle the architecture for either path.
Can you integrate OpenAI with my existing app?
Yes! We integrate via REST APIs, SDKs (Python, Node.js), or middleware (LangChain). We work with your tech stack—whether it's a web app, mobile backend, or internal tool.
How do you handle conversation memory?
We implement session-based memory (current conversation) or persistent memory (remember past conversations) using databases or vector stores. This enables AI that builds context with users over time.
What's the typical API cost for OpenAI?
GPT-4 Turbo costs ~$0.01/1K input tokens and $0.03/1K output tokens. Most applications cost $50-$500/month for moderate usage. We provide cost projections and monitoring dashboards during the project.
How long does OpenAI integration take?
Basic integrations take 3-5 days. Applications with function calling and memory run 1-2 weeks. Full RAG implementations typically take 2-4 weeks including data ingestion and testing.
Client Reviews
★★★★★ 5
based on 167 reviews
★★★★★ 5
Support that actually scales
We integrated GPT-4 into our Shopify-based support flow and expected a lot of hand-holding. AHK.AI delivered a clean OpenAI API integration with function calling for order status, returns, and address changes. They also set up conversation memory so customers don’t repeat themselves, plus cost controls that kept token spend predictable during peak weekends. The handoff included clear runbooks and monitoring tips. Our first-response time dropped noticeably without sacrificing accuracy.
Project: GPT-4 customer support assistant with function calls to OMS/CRM and conversation memory for repeat shoppers
★★★★★ 5
RAG done the right way
We needed a RAG pipeline for in-app documentation search across 3 years of release notes and API docs. Their team implemented embeddings, chunking, and a vector database with sensible retrieval filters (version, product tier). The prompts were tuned to cite sources and avoid hallucinations, and the tool-use layer can open a support ticket when confidence is low. Latency stayed under our target, and the integration is production-ready with logging and fallback behavior.
Project: In-app RAG assistant for product docs using embeddings + vector DB with version-aware retrieval
★★★★ 4.5
Strong, compliant implementation
We used AHK.AI to add a clinician-facing summarization feature for visit notes. They were careful about PHI handling, built redaction steps into the pipeline, and implemented prompt patterns that reduce risky outputs. The function calling setup pulls problem lists and meds from our EHR API, and the conversation memory is scoped per patient encounter. Only minor delays on our side slowed testing, but the end result is stable and auditable.
Project: EHR-integrated visit note summarizer with guarded prompts, tool calls, and encounter-scoped memory
★★★★★ 5
Lead routing got smarter
We run high-volume inbound leads from Zillow and our own site. AHK.AI built a GPT-4 qualification layer that parses free-text inquiries, extracts timeline/budget, and uses function calling to assign agents in our CRM based on zip code and specialty. The RAG component pulls property details and HOA notes from our listings database so replies are accurate. It’s way more consistent than our old templates, and costs are lower than expected.
Project: Lead qualification + automated agent assignment using GPT-4, CRM tool calls, and listing-data RAG
★★★★ 4
Solid build, needs polish
They integrated GPT-4 into our internal research portal to summarize filings and answer questions using a RAG pipeline over SEC documents. Retrieval quality is good and the prompts enforce “quote-and-cite,” which our compliance team appreciated. We did need an extra iteration to tighten guardrails around speculative language and to tweak cost optimization for long 10-Ks. Overall, the system is reliable and the API integration is clean; just plan for a couple tuning cycles.
Project: SEC filing Q&A with embeddings + vector DB retrieval and compliance-focused prompt constraints
★★★★★ 5
Briefs to drafts fast
Our agency wanted an AI workflow that turns client briefs into campaign concepts without sounding generic. AHK.AI set up prompt frameworks, brand voice constraints, and a tool-use layer that pulls approved copy blocks from our knowledge base via embeddings. The conversation memory keeps context across revisions, which is huge for multi-stakeholder feedback. We’re producing first drafts in minutes and spending our time on strategy instead of formatting. The integration into our project portal was smooth.
Project: Creative ideation + copy drafting assistant with brand-voice prompts and RAG over approved assets
★★★★ 4.5
Better tech support answers
We sell industrial sensors and our support team lives in PDFs and wiring diagrams. AHK.AI built a RAG system that indexes manuals, calibration procedures, and error-code tables, then answers technician questions with citations. Function calling connects to our ticketing system and can request serial-number lookups from our warranty database. The responses are far more consistent than our old macros. We’re still expanding the document set, but the foundation is strong.
Project: Technical support RAG assistant over product manuals with ticketing and warranty database tool integration
★★★★★ 5
Proposal workflow streamlined
We needed a secure way to generate tailored proposals from a library of case studies and SOW templates. AHK.AI implemented embeddings and retrieval so GPT-4 only uses approved content, plus a function calling layer to pull pricing rules from our spreadsheet API. They also optimized prompts to reduce verbosity and token usage, which matters when iterating with clients. The deliverable included tests and deployment notes, so our engineers could maintain it confidently.
Project: Proposal generator using RAG over internal templates and tool calls to pricing rules API
★★★★ 4.5
Great for student help
We added a tutoring assistant inside our LMS to answer course questions and point students to the right module. AHK.AI set up a RAG pipeline over lecture notes, rubrics, and FAQ pages, and tuned prompts to encourage step-by-step guidance without giving away full solutions. Conversation memory keeps the thread coherent across sessions. Minor UI tweaks were on us, but the backend integration is stable and the cost optimization kept usage within our budget.
Project: LMS tutoring assistant with RAG over course materials and memory for multi-turn student sessions
★★★★★ 5
Ops visibility improved
We manage last-mile deliveries and wanted an ops chatbot that could answer “where is shipment X” and “why was it delayed” in plain English. AHK.AI integrated GPT-4 with function calling into our TMS, so it can fetch scan events and exception codes, then explain them clearly. They added conversation memory for ongoing incident threads and a retrieval layer for SOPs so recommendations match our playbooks. It reduced Slack noise and sped up escalation decisions.
Project: Operations chatbot with TMS tool calls, SOP RAG retrieval, and incident-thread conversation memory
