AI Project Manager — Project Management with LLM

Imagine: you have 6 parallel projects, a team of 20 developers, and only two PMs. Every morning they collect statuses, prepare reports, check risks. 35% of their time goes to administration. We built AI PM — a digital employee (Jira AI agent based on LLM) that takes over this workload. This AI Project Manager (AI PM) automates routine tasks, from standups to risk analysis. Result from our practice: administrative PM time dropped to 18%, and PMs were able to take on a third project. Without quality loss or creating a "surveillance" feeling in the team. According to PMI Pulse of the Profession, PM administrative load is reduced by 40% when implementing AI agents.

Status reports eat hours

Every morning — gathering information from Jira, GitHub, Slack. AI PM generates a standup digest automatically in 5 seconds. The team receives it in Slack, the PM gets a detailed version.

AI Project Manager transforms sprint planning

Without considering velocity history and actual developer availability. AI PM analyzes the last 5 sprints, team capacity, and proposes an optimal set of tasks. Overload errors — 30% fewer. AI Project Manager improves planning accuracy by 1.4 times compared to manual methods, and detects risks 3 times faster.

Risks are noticed too late

A 30% velocity drop over 2 sprints is a signal, but often goes unnoticed. AI PM monitors numerical metrics and uses LLM to analyze hidden patterns (e.g., key developer going on sick leave). A warning arrives 3 days before a failure.

AI Project Manager stack and architecture

We use OpenAI GPT-4o for generating structured data (tasks, risks), LangChain for call chains, and Jira/GitHub REST API for data. We apply MLOps practices: Weights & Biases, MLflow. Below — key components with code.

Decomposing requirements into tasks

from openai import AsyncOpenAI
from pydantic import BaseModel
from typing import Literal, Optional

client = AsyncOpenAI()

class ProjectTask(BaseModel):
    title: str
    description: str
    acceptance_criteria: list[str]
    story_points: int          # Fibonacci: 1, 2, 3, 5, 8, 13
    task_type: Literal["feature", "bug", "tech_debt", "research", "devops"]
    required_skills: list[str]
    dependencies: list[str]    # Names of dependent tasks
    priority: Literal["critical", "high", "medium", "low"]
    risk_notes: Optional[str]

async def decompose_requirement(
    requirement: str,
    team_skills: list[str],
    existing_codebase_context: str = "",
) -> list[ProjectTask]:

    response = await client.beta.chat.completions.parse(
        model="gpt-4o",
        messages=[{
            "role": "system",
            "content": f"""You are an experienced tech lead and PM.
Decompose the requirement into concrete tasks for the team.

Decomposition principles:
- Each task should be doable within 1-3 days by one developer
- Acceptance criteria — specific, verifiable
- Specify dependencies between tasks
- Story points: use Fibonacci, base on complexity

Team competencies: {team_skills}
Codebase context: {existing_codebase_context[:500] if existing_codebase_context else 'not provided'}"""
        }, {
            "role": "user",
            "content": f"Requirement: {requirement}",
        }],
        response_format=list[ProjectTask],
        temperature=0.2,
    )

    return response.choices[0].message.parsed

Sprint Planning agent

class SprintPlanningAgent:

    async def plan_sprint(
        self,
        backlog: list[dict],
        team_capacity: dict,  # {developer: available_hours}
        sprint_goal: str,
        velocity_history: list[int],
    ) -> dict:
        """Creates a sprint plan considering team capacity"""

        available_sp = self.estimate_capacity(team_capacity, velocity_history)

        sprint_plan = await client.chat.completions.create(
            model="gpt-4o",
            messages=[{
                "role": "system",
                "content": """You are a Scrum Master planning a sprint.
Select tasks from the backlog into the sprint, observing:
1. Sprint goal — tasks must align with it
2. Team capacity must not be exceeded
3. Account for dependencies — cannot take a task if its dependency is incomplete
4. Balance: do not take only bugs or only features"""
            }, {
                "role": "user",
                "content": f"""Sprint goal: {sprint_goal}
Available capacity: {available_sp} SP
Team and availability: {team_capacity}
Backlog (top-30 by priority):
{json.dumps(backlog[:30], ensure_ascii=False, indent=2)}

Return JSON: {{"selected_tasks": [...task_ids], "assignments": {{developer: [task_ids]}}, "sprint_risk": "low/medium/high", "risk_explanation": "..."}}"""
            }],
            response_format={"type": "json_object"},
        )

        return json.loads(sprint_plan.choices[0].message.content)

    def estimate_capacity(self, team_capacity: dict, velocity_history: list[int]) -> int:
        avg_velocity = sum(velocity_history[-5:]) / len(velocity_history[-5:])
        total_hours = sum(team_capacity.values())
        standard_sprint_hours = 8 * 10 * len(team_capacity)  # 2 weeks
        capacity_ratio = total_hours / standard_sprint_hours
        return int(avg_velocity * capacity_ratio)

Daily Standup automation

class StandupBot:

    async def collect_and_summarize(self, project_id: str) -> str:
        """Collects progress data and generates standup digest"""

        # Data from Jira/GitHub
        jira_updates = await jira.get_yesterday_updates(project_id)
        github_commits = await github.get_commits(project_id, since="yesterday")
        blockers = await jira.get_current_blockers(project_id)
        open_prs = await github.get_open_prs(project_id)

        digest = await client.chat.completions.create(
            model="gpt-4o-mini",
            messages=[{
                "role": "system",
                "content": "Create a concise standup digest. Format: ✅ Done, 🔄 In progress, 🚧 Blockers. Specific, no fluff."
            }, {
                "role": "user",
                "content": f"""Updates from Jira:
{json.dumps(jira_updates, ensure_ascii=False, indent=2)}

Commits:
{json.dumps(github_commits[:10], ensure_ascii=False, indent=2)}

Blockers:
{json.dumps(blockers, ensure_ascii=False, indent=2)}

Open PRs: {len(open_prs)}, of which awaiting review > 24h: {sum(1 for p in open_prs if p['waiting_hours'] > 24)}"""
            }],
        )

        return digest.choices[0].message.content

    async def post_to_slack(self, digest: str, channel: str):
        await slack_client.chat_postMessage(
            channel=channel,
            text=f"*Standup Digest — {datetime.now().strftime('%d.%m.%Y')}*\n{digest}",
        )

Risk Monitor

class ProjectRiskMonitor:

    async def assess_risks(self, project_data: dict) -> list[dict]:
        """Automatically identifies and evaluates project risks"""

        # Numerical risk signals
        numeric_risks = []
        sprint = project_data.get("current_sprint", {})

        velocity_trend = project_data.get("velocity_trend", [])
        if len(velocity_trend) >= 3 and velocity_trend[-1] < velocity_trend[-3] * 0.7:
            numeric_risks.append({
                "type": "velocity_decline",
                "severity": "high",
                "data": f"Velocity: {velocity_trend[-3]} → {velocity_trend[-1]} SP",
            })

        team_absences = project_data.get("planned_absences", [])
        sprint_end = project_data.get("sprint_end_date")
        critical_absence = any(
            a for a in team_absences
            if a.get("days") >= 3 and a.get("person") in sprint.get("key_developers", [])
        )
        if critical_absence:
            numeric_risks.append({
                "type": "key_person_absence",
                "severity": "medium",
                "data": "Key developer absent during critical period",
            })

        # LLM analyzes risk patterns
        risk_assessment = await client.chat.completions.create(
            model="gpt-4o",
            messages=[{
                "role": "system",
                "content": "Identify hidden risks in project data. Return JSON list of risks with severity and mitigation."
            }, {
                "role": "user",
                "content": json.dumps({**project_data, "known_risks": numeric_risks}, ensure_ascii=False),
            }],
            response_format={"type": "json_object"},
        )

        ai_risks = json.loads(risk_assessment.choices[0].message.content).get("risks", [])
        return numeric_risks + ai_risks

How AI PM improves planning accuracy?

AI Project Manager processes 30+ variables simultaneously: velocity history, team availability, dependencies, risks. Humans can't hold everything in their head. AI PM does it in seconds without errors. In our case, sprint failures decreased by 44%, and story point estimation accuracy increased to 85% (1.4 times better than manual planning).

What's included

Why implement AI PM now?

Compare with the traditional approach:

Our certified team (PMP, Scrum Master) guarantees implementation within agreed timeline and a 20% reduction in PM administrative load in the first month. We stand behind the results with a satisfaction guarantee.

Process

1. Analytics (1–2 days) — study your process, tools, team.
2. Design (1 week) — define architecture, choose deployment mode (on-premise / cloud).
3. Development (2–4 weeks) — build modules for your stack using GPT-4o and LangChain.
4. Testing (1 week) — validate on historical data, A/B test with current PM.
5. Deployment and training (1 week) — deploy, train the team, hand over documentation.

Timeline estimates

• Sprint planning and decomposition: 2–3 weeks
• Standup bot and monitoring: 1–2 weeks
• Risk assessment and alerts: 1–2 weeks
• Jira/GitHub/Slack integrations: 1–2 weeks
• Total: 5–9 weeks depending on complexity

Contact us to discuss your project — we'll evaluate it for free and propose an optimal plan. Team experience: 10+ years in AI and project management, PMP and Scrum Master certifications.

Practical case: digital product studio, 6 parallel projects

Situation: 2 PMs managed 6 projects total. 35% of time went to status reports, sprint planning, blocker communication.

AI PM took over:

• Automatic standup digest in Slack every morning
• Weekly stakeholder report
• Risk warnings (velocity gap, blockers > 2 days)
• Epic decomposition when creating new tasks
• Sprint planning preparation (task proposals based on capacity)

Results:

• PM administrative time: 35% → 18%
• PMs were able to take on a 3rd project per PM
• Sprint failures: -44% (early risk warnings)
• Team: 4.1/5.0 usefulness rating of AI PM (no "surveillance" feeling)

This translates to an estimated saving of $32,000 per PM annually (assuming $80k salary). In summary: AI Project Manager is not a replacement, but a tool that makes PMs more effective. Get a consultation — we'll show how it works on your data. Order a demo of AI PM on your data now.

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.