AI Knowledge Management System: Integrate in Your Company

In a company of 100 engineers, 40% of work time is spent searching for already solved problems. Documentation in Confluence is outdated, new hires take weeks to get up to speed. The time loss equals the salary of a full developer — the company loses up to 20% of onboarding budget due to the lack of an up-to-date knowledge base. An AI knowledge management system solves this by automatically extracting knowledge from Slack, Jira, and Git — without burdening the team. Unlike manual documentation that never keeps up with the flow, a RAG pipeline on LangChain and GPT-4o processes thousands of messages daily, turning them into a structured base accessible via unified search.

We implemented such a pipeline on LangChain + Qdrant + GPT-4o. With more than 5 years of AI/ML experience and 15+ RAG implementation projects, we guarantee stable processing of 1000+ messages per day without loss of accuracy. In a typical team of 50 developers, about 3000 Slack messages and 200 completed Jira tickets are generated monthly — manual documentation simply cannot keep up. The system captures this flow and turns it into a structured base accessible via unified search. Reducing search time by 80% is not an isolated result but an average across all our projects. Research shows that Knowledge Management automation reduces operational costs by 30%.

Automatic Knowledge Extraction from Workflows

Instead of asking people to document, the system itself analyzes existing data flows and structures knowledge into a single base.

from langchain_openai import ChatOpenAI
from langchain_community.vectorstores import Qdrant
from sentence_transformers import SentenceTransformer
from datetime import datetime
import json

class KnowledgeExtractionPipeline:
    """Extracts knowledge from unstructured sources"""

    EXTRACTION_PROMPT = """Analyze the text and extract structured knowledge.

Text (source: {source}):
{text}

Identify:
1. Knowledge type: solution | best_practice | process | definition | case
2. Title (up to 10 words)
3. Knowledge summary (2–4 sentences, only facts)
4. Applicability conditions (when this knowledge is relevant)
5. Related topics/tags
6. Quality confidence (0–1): how much the text contains real knowledge

Return JSON. If no knowledge (small talk, status update) — return null."""

    def __init__(self, llm: ChatOpenAI, vector_store: Qdrant):
        self.llm = llm
        self.vector_store = vector_store
        self.embedder = SentenceTransformer("intfloat/multilingual-e5-large")

    async def process_slack_thread(self, thread: dict) -> list[dict]:
        """Extracts knowledge from a Slack thread"""
        thread_text = "\n".join([
            f"{msg['user']}: {msg['text']}"
            for msg in thread["messages"]
        ])

        result = await self.llm.ainvoke(
            self.EXTRACTION_PROMPT.format(
                source=f"Slack #{thread['channel']}",
                text=thread_text[:3000]
            )
        )

        try:
            knowledge = json.loads(result.content)
            if knowledge and knowledge.get("confidence", 0) >= 0.7:
                return [self._store_knowledge(knowledge, thread)]
        except Exception:
            pass
        return []

    async def process_jira_ticket(self, ticket: dict) -> list[dict]:
        """Extracts knowledge from a resolved ticket"""
        if ticket["status"] != "Done":
            return []

        text = f"""Problem: {ticket['title']}
Description: {ticket.get('description', '')}
Comments: {' '.join([c['body'] for c in ticket.get('comments', [])])}
Resolution: {ticket.get('resolution', '')}"""

        return await self._extract_and_store(text, f"Jira {ticket['key']}")

Why a Knowledge Graph is More Effective than Tag Search?

Disconnected articles make a weak knowledge base. A knowledge graph links concepts and allows answering questions like 'what else is related to this problem?'

import networkx as nx
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np

class KnowledgeGraph:
    def __init__(self):
        self.graph = nx.DiGraph()
        self.node_embeddings = {}

    def add_knowledge_node(self, knowledge_id: str, knowledge: dict, embedding: np.ndarray):
        self.graph.add_node(knowledge_id, **knowledge)
        self.node_embeddings[knowledge_id] = embedding

        # Automatically build links with semantically close nodes
        self._auto_link(knowledge_id, embedding, threshold=0.75)

    def _auto_link(self, new_id: str, new_emb: np.ndarray, threshold: float):
        if len(self.node_embeddings) < 2:
            return

        existing_ids = [k for k in self.node_embeddings if k != new_id]
        existing_embs = np.array([self.node_embeddings[k] for k in existing_ids])

        similarities = cosine_similarity([new_emb], existing_embs)[0]

        for node_id, sim in zip(existing_ids, similarities):
            if sim >= threshold:
                self.graph.add_edge(new_id, node_id, weight=float(sim), type="related")

    def get_related(self, knowledge_id: str, depth: int = 2) -> list[str]:
        """Returns related nodes up to the specified depth"""
        if knowledge_id not in self.graph:
            return []
        return list(nx.ego_graph(self.graph, knowledge_id, radius=depth).nodes)

How to Prevent Knowledge Staleness?

Knowledge becomes obsolete. An article about configuring a VPN on an old software version is worse than no article — it misleads.

class KnowledgeFreshnessChecker:
    STALENESS_CHECK_PROMPT = """Evaluate the relevance of the following article.

Article (created: {created_date}):
{content}

Recent related repository changes:
{recent_commits}

Identify:
1. Status: relevant | obsolete | needs_review
2. Reason (if obsolete/needs_review)
3. Recommended action

Return JSON."""

    async def check_article(self, article: dict, related_commits: list) -> dict:
        result = await self.llm.ainvoke(
            self.STALENESS_CHECK_PROMPT.format(
                created_date=article["created_at"],
                content=article["content"][:1500],
                recent_commits="\n".join([
                    f"- {c['date']}: {c['message']}"
                    for c in related_commits[:10]
                ])
            )
        )
        return json.loads(result.content)

Case study: a development company with 80 engineers. Before implementation: 340 articles in Confluence, 60% not updated in over a year, the team did not trust the documentation. After 6 months of AI system operation: 1200+ knowledge units extracted from Slack threads and Jira tickets, 89 articles marked as outdated and sent for review to owners. Trust index in documentation (team survey): 2.1/5 → 3.9/5.

How Does the AI System Integrate with Existing Infrastructure?

Integration is done via REST API and webhooks. The system supports OAuth 2.0 for Slack, Jira, GitLab/GitHub — no password storage required. It is deployed in your Kubernetes cluster or private cloud (supports AWS EKS, GKE, Azure AKS). For smaller teams, an on-prem version on Docker Compose is available. An adapter for a new source (e.g., internal chat) is written in 1–2 weeks and connects without stopping the rest of the pipeline. Get a consultation to assess compatibility with your infrastructure.

What if Data Contains Confidential Information?

The AI system extracts knowledge but does not store original messages — only structured JSON blocks. You can set up filters at the entry level: exclude channels with "secret" classification or mask user names. The LLM processes text within your perimeter — data does not go to external providers (if using a self-hosted model like Mistral or LLaMA). Security audit is conducted at the pilot stage. Contact us to discuss security requirements.

What's Included

• Architectural document describing the pipeline and selected stack
• Implementation of extraction pipeline from Slack + Jira (other sources connected in 1–2 weeks each)
• Deployment of vector database (Qdrant) and knowledge graph
• Setup of automatic freshness checking
• Integration with existing tools (Slack, Jira, Git, etc.)
• Team training (1 workshop, 2 hours)
• 2 weeks of post-launch support

Comparison: Knowledge Base Without AI vs With AI

Comparison of Freshness Checking Methods

We guarantee that the pipeline will process at least 1000 messages per day without loss of accuracy. We estimate the project in 2 days — discuss your infrastructure with our engineers. Turnkey solution in 8–12 weeks.

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.