Securely ground your AI in enterprise truth. We build retrieval-augmented generation (RAG) systems that connect GPT-5.2 and other LLMs to your private data—delivering accurate, cited answers with zero-retention privacy. From policy search to legal discovery, we facilitate conversation with your organizational brain.
GPT-5.2 offers vastly superior context handling and reasoning compared to previous models. This means fewer hallucinations, better understanding of complex internal documents, and the ability to synthesize answers from multiple contradictory sources accurately.
How do you secure our private data?
We prioritize data sovereignty. We can build on Azure OpenAI or private VPCs where your data never leaves your environment. The vector database and retrieval systems are architected with 'Zero-Retention' policies for the LLM providers.
What is 'Hybrid Search' and why does it matter?
Pure vector search (semantic) isn't enough for exact matches like part numbers or SKUs. We implement Hybrid Search (Vector + Keyword) using re-ranking models (like Cohere) to ensure your staff finds exactly what they are looking for, every time.
Can the system respect permissions (ACL/RBAC)?
Yes. We design the ingestion pipeline to capture document-level permissions. When a user queries the RAG system, the retrieval step filters content based on their existing access rights, ensuring no one sees data they shouldn't.
How do you prevent hallucinations?
We enforce a 'Strict Citation' protocol. The model is instructed to only answer using retrieved context and must provide a citation for every claim. If the information isn't in your docs, the system is trained to say 'I don't know' rather than guessing.
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
