AI-Powered Wiki Platform for Corporate Knowledge Base

Developers spend an average of 2 hours per day searching for up-to-date documentation. A Confluence of 2000 pages — half of it outdated. An AI-powered wiki solves this: the system automatically keeps content current, generates drafts for new articles, links semantically similar pages, and answers questions directly via a RAG assistant. This AI wiki uses semantic search to instantly find relevant information. We build these platforms turnkey using an open-source stack and self-hosted deployment — your data stays under your control.

Problems that an AI wiki solves

The first problem is semantic search. Keyword search fails to find a document if the query wording differs. An AI wiki uses embeddings (model intfloat/multilingual-e5-large, 768 dimensions) and compares query and article vectors, returning relevant results even with synonyms. Semantic search is 3-5 times more accurate than keyword search, reducing search time by 6x.

The second problem is content staleness. After a library release or API change, documentation often remains outdated. A staleness detector monitors Git repositories: if a file referenced in an article changes, the page owner receives a notification with a commit URL. This cuts update time from weeks to hours.

The third is onboarding new hires. A new employee spends 3 weeks studying the knowledge base. An AI assistant in a sidebar answers questions from the documentation in seconds, citing sources. Onboarding time drops to 1.5 weeks — 2 times faster than before.

How an AI wiki automatically maintains relevance

The key component is StalenessMonitor, which asynchronously checks reference files. Upon detecting changes, it sends the article owner a notification with action review_required. This mechanism works with GitHub, GitLab, and custom Git repositories.

import asyncio
from github import Github

class StalenessMonitor:
    async def check_code_changes(self, article: dict, github_token: str):
        g = Github(github_token)
        code_refs = self._extract_code_references(article["content"])
        for ref in code_refs:
            try:
                repo = g.get_repo(ref["repo"])
                commits = repo.get_commits(path=ref["path"], since=article["last_updated_at"])
                if commits.totalCount > 0:
                    await self._notify_owner(article, ref, commits[0])
            except Exception:
                pass

    async def _notify_owner(self, article, ref, commit):
        notification = {
            "article_id": article["id"],
            "owner": article["owner_email"],
            "message": f"File {ref['path']} has changed since the article's last update",
            "commit_url": commit.html_url,
            "action": "review_required"
        }
        await self.notification_service.send(notification)

Why RAG-oriented architecture

RAG (Retrieval-Augmented Generation) is the standard for corporate knowledge bases. The AI wiki's RAG pipeline combines semantic search and generation for accurate answers. When a question is asked, the system first retrieves the top 6 semantically similar documents, then passes them to an LLM (GPT-4o-mini) to produce an answer with citations. We use LlamaIndex for building the index, Qdrant as vector store (supports filtering, scales horizontally). Answers always include source nodes with article title and score.

from llama_index.core import VectorStoreIndex
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.llms.openai import OpenAI
from llama_index.core.query_engine import RetrieverQueryEngine

class WikiPlatform:
    def __init__(self, db_engine, qdrant_client, openai_key):
        self.embed_model = HuggingFaceEmbedding(model_name="intfloat/multilingual-e5-large")
        self.llm = OpenAI(model="gpt-4o-mini", api_key=openai_key)
        self.index = VectorStoreIndex.from_vector_store(qdrant_client)

    def answer_question(self, question):
        query_engine = RetrieverQueryEngine.from_args(
            retriever=self.index.as_retriever(similarity_top_k=6),
            llm=self.llm
        )
        response = query_engine.query(question)
        return {
            "answer": str(response),
            "sources": [
                {"title": n.metadata.get("title"), "url": n.metadata.get("url"), "score": round(n.score, 3)}
                for n in response.source_nodes
            ]
        }

Why open-source stack over proprietary

Vendor lock-in is a risk. If tomorrow a proprietary service changes its API or licensing terms, migration becomes painful. We choose LlamaIndex, Qdrant, HuggingFace, FastAPI — all components are self-hosted and security-audited. We guarantee a minimum 3x improvement in search speed compared to legacy systems. With over 5 years of experience and 20+ successful deployments, we deliver robust solutions. Our team holds certifications in machine learning and DevSecOps, ensuring quality and security.

Case study: a fintech startup with 30 developers. After deploying our AI wiki, the time to answer internal questions dropped from 15 minutes to 1 minute — a 15x improvement. The annual team time savings were $60,000 per year. Additionally, onboarding a new employee costs 200,000 rubles less due to reduced mentoring. Get a consultation on implementing an AI wiki in your company.

Work process: from audit to deployment

1. Analytics — audit existing knowledge base: volume, content types, update frequency, MLOps practices
2. Design — stack selection, data schema (Pgvector/Qdrant), SSO integration API
3. Implementation — custom modules: auto-draft generation, Tiptap AI Extension, staleness monitoring
4. Testing — load testing (latency p99), generation quality evaluation (human evaluation), security checks
5. Deployment — bare-metal/k8s deployment, Helm charts, CI/CD pipeline

Time cost comparison for content maintenance

Search accuracy comparison

What's included in the result

• Platform source code (Python, TypeScript) with API documentation
• Docker images and Helm charts for deployment
• Migration scripts from Confluence/Notion
• Team training (2-3 sessions) and written instructions
• Technical support for 2 weeks post-launch

Timelines

• Basic AI wiki (search + auto-linking): 4-6 weeks
• With draft generation and built-in assistant: 8-10 weeks
• Self-hosted deployment with GitHub/GitLab integration: +2-3 weeks

Evaluate your project: describe your current knowledge base to us — we'll prepare a proposal with scope and timelines in 2-3 days. Contact us to get a detailed analysis of your knowledge base and a project estimate.

LLM Development: Fine-Tuning, RAG, Agents, and Production Deployment

Using GPT‑4 or Claude 3.5 Sonnet through a public API is not a solution — it's just a tool. When the requirement is to "make it like ChatGPT, but on our data," there is a real engineering challenge behind it: from prompt engineering to training a 70B model on your own infrastructure. End-to-end LLM solution development is a complex stack, and we have been doing it for over 5 years. During this time, we have completed over 20 projects in generative AI: from RAG systems for legal departments to custom support agents. Where exactly your task falls depends on data, latency requirements, budget, and how critical confidentiality is.

A typical situation: the client has already tried ChatGPT, but results are unstable — sometimes accurate, sometimes hallucinating. Or they need integration into a corporate portal while complying with security policies. Let's break down each layer of the stack in detail — from RAG to production deployment.

Why Do RAG Systems Break and How to Fix It?

RAG (Retrieval-Augmented Generation) looks simple: find relevant documents, put them in context, get an answer. In practice, it fails in several places.

Chunking without overlap. Classic mistake: chunk_size=512, overlap=0. If the answer lies across two chunks, retrieval won't find either with sufficient confidence. Solution: overlap 15–25% of chunk_size, or better yet, sentence-aware splitting with spaCy or NLTK instead of naive character splitting.

Poor embedder. text-embedding-ada-002 is good for general use, but on legal or medical texts, specialized models like E5-large-v2, BGE-M3, or fine-tuned sentence-transformers on domain data outperform it. Recall@5 differences can be 15–25%.

No re-ranking. Vector search optimizes for speed, not relevance. A cross-encoder re-ranker (ms-marco-MiniLM-L-6-v2, bge-reranker-large) after initial retrieval improves top-3 accuracy with acceptable latency (+50–150ms). This is often more impactful than improving the embedding model.

Hybrid search. Dense vectors alone work poorly on exact queries: names, SKUs, codes. BM25 (sparse) finds exact matches but misses semantics. Hybrid via RRF (Reciprocal Rank Fusion) is the optimal compromise. Qdrant, Weaviate, and pgvector 0.7+ support hybrid search natively.

When to Fine-Tune Instead of Prompt Engineering?

Prompt engineering solves ~70% of LLM adaptation tasks for a domain. The remaining 30% require fine-tuning. Three indicators: the model ignores a specific output format even with detailed prompting; the task requires deep knowledge of specialized vocabulary (medicine, law); you need to significantly reduce token costs by replacing a large model with a smaller specialized one.

LoRA and QLoRA are the standard for SFT. LoRA adds trainable low-rank matrices to attention layers. A typical configuration for Llama-3 8B: r=64, lora_alpha=128, target_modules=["q_proj","v_proj","k_proj","o_proj"] yields ~0.8% trainable parameters, training on one A100 40GB. QLoRA adds 4-bit quantization (NF4) and allows fine-tuning 70B models on two A100 40GB, though speed drops by half compared to bf16.

DPO instead of RLHF. Direct Preference Optimization requires only (chosen, rejected) pairs, not scalar reward signals. DPOTrainer from the trl library (Hugging Face) implements it in a few dozen lines.

Common mistake. A dataset of 500 examples, 5 epochs, validation loss 0.8 — seems fine. But on test, the model degrades on general instructions. Cause: catastrophic forgetting. Solution: add 10–20% general instruction-following examples (Alpaca, FLAN) to the training set to preserve original capabilities.

How to Choose a Base Model: 8B or 70B?

For most tasks, fine-tuning an 8B model is sufficient. 70B is needed when deep reasoning is required or the 8B baseline does not reach the required quality even after fine-tuning. Inference cost for Llama-3 8B via vLLM on A100 is efficient; the exact cost depends on volume.

What Does PagedAttention Bring to Production?

vLLM is the first choice for serving open-source models. PagedAttention is the key technical innovation: KV-cache is managed like virtual memory in an OS, without fragmentation. This yields 2–4x higher throughput compared to naive HuggingFace Transformers inference. The vLLM documentation confirms that continuous batching and PagedAttention are the standard for high-load LLM services.

Typical numbers on A100 80GB for Llama-3 8B (bf16): 400–600 req/s, P50 latency 200–400ms, P99 latency 600–900ms at concurrency 64. For 70B on two A100 with tensor parallelism: 80–120 req/s, P99 latency 1.5–2.5s. AWQ or GPTQ quantization reduces memory consumption by 2x with quality loss within 1–3%.

Multi-Agent Systems

Agents are LLMs with access to tools: search, code execution, API calls, database interaction. Common patterns:

• ReAct (Reason + Act): the model reasons → chooses a tool → observes the result → reasons again. LangChain and LlamaIndex implement it out of the box.
• Multi-agent orchestration: multiple specialized agents with a coordinator on top. Example: coordinator → researcher (search + summarization) → coder (code generation and execution) → critic (verification). Tools: AutoGen (Microsoft), CrewAI, custom implementation on LangGraph.

In production, agent systems are non-deterministic. Essential: guardrails, step limits, logging of each step, human-in-the-loop for critical actions.

How We Work: Stages, Timeline, Deliverables

What Is Included

We deliver:

• Technical documentation (model card, configs, deployment instructions)
• Access to infrastructure (code repository, trained weights)
• 1 month of post-deployment support (consultations, bug fixes)
• Customer team training (2–3 sessions on system operation)

Timeline: basic RAG prototype — 1–2 weeks. Fine-tuning with customer data — 3–6 weeks (including data preparation). Production system with monitoring and retraining — 2–4 months. Cost is calculated individually based on data volume, model complexity, and infrastructure requirements.

We guarantee the quality of the final model with performance benchmarks and ongoing monitoring. Our engineers have hands‑on experience with dozens of production LLM systems.

Want to evaluate your project? Leave a request — we will prepare a preliminary summary within 1–2 business days. Or get a consultation on choosing the approach: RAG, fine-tuning, or hybrid — we will tell you what works best for you. Contact us to discuss your LLM development needs. Schedule a free consultation today.