Unified analytical dashboard (end-to-end analytics + sales planning)

Budget: 26000 UAH Deadline: 14 days

Hello.
The task is extensive but architecturally clear. I have read the requirements for the dashboard, roles, and AI queries.
In response to the question in point 10 (Custom or BI tool): for your task, only a custom frontend is needed. Ready-made BI systems (Looker, PowerBI) build graphs well, but they are very rigidly customizable for specific role systems (traffic lights, personal KPIs for managers) and practically do not allow for adequately embedding a chat with LLM for free queries to the database.
Therefore, my proposal for the stack:
Storage (DB): PostgreSQL. Ideal for a relational structure (deals, payments, users). I work through Prisma ORM for strict typing at the code level, so no data from webhooks is lost.
Backend (ETL): Node.js (Express). The service will accept webhooks from Kommo/Stripe and, via cron (every 15-30 minutes), will pull expenses from the Meta API.
Frontend: Next.js (App Router) + Tailwind CSS + TanStack Query. The dashboard will operate without lags when switching dates due to proper server-side caching.
AI Module: Google Gemini API. A very fast model with a large contextual window for analyzing aggregated data from the database.
Similar projects:
My main profile is Full-Stack development of SaaS products. I built the PropText.ai system, where I connected custom dashboards, Stripe billing, and generation through the Google Gemini API.
Estimated timelines and costs (approximate, subject to clarification):
I tie all calculations to USD for convenience (or equivalent in UAH or EUR).
Stage 1 (Audit, DB architecture, field mapping): ~$200, 3-5 days.
Stage 2 (MVP: DB, webhooks from Kommo, Stripe, Meta, basic UI with roles): ~$800 - $1000, 2-3 weeks.
Stage 3 (Cycles, forecasts, plan/fact): ~$600, 1-1.5 weeks.
Stage 4 (SEO, AI reports, alerts): ~$600, 1.5 weeks.
Stage 5 (New channels): evaluated separately.
Monthly ownership cost (OPEX):
Hosting: Vercel (for frontend) + reliable VPS (for backend and PostgreSQL DB) — will total about $20-30 per month.
AI tokens (Gemini): Depends on the frequency of requests, but for such volumes of analytics, it's pennies, approximately $5-15/month.
Licenses: $0 (the stack is completely open-source).
One question to start: do you currently have UTM tags set up or a hidden field with the deal ID so that Stripe can accurately transmit metadata during payment for linking with Kommo? Expand

Budget: 27000 UAH Deadline: 60 days

Hello. My name is Alexey, and I represent a group of developers – NC-1. For over five years, we have been creating websites, mobile applications, online stores, ERP/CRM systems, and other e-commerce products.
In our team, we have full stack, senior developers with the necessary experience and knowledge for you.
Recommended technical stack:
Backend and ETL: A combination of Python (for data collection scripts, predictive analytics, and LLM) and NestJS (for implementing a reliable and fast API dashboard).
Frontend: Next.js (React) — the ideal option for quickly rendering graphs, complex user interfaces, role models, and conducting custom chats with AI.
Database (DWH): PostgreSQL. Considering the data volumes from CRM, its power will be more than sufficient. For queues (synchronization every 15 minutes) and caching, we will add Redis.
Preliminary cost: 390,000 UAH
Monthly ownership cost: 60–130 USD/month.
Best regards, Alexey M.

Budget: 27000 UAH Deadline: 20 days

I understood the task - a single dashboard is needed with comprehensive analytics for the YCL direction: advertising, funnel in Kommo, payments through Stripe, manager performance, and revenue planning, all with automatic data retrieval via API. I will set up data extraction from Kommo with a filter only for deals in the YCL funnels and maintain a history of transitions through stages and reasons for refusals. I will separately connect Stripe for payments and advertising sources (Meta Ads, Google Ads, and others) so that all expenses and leads are collected in one data warehouse. I will create the dashboard so that the data updates automatically, without manual spreadsheet consolidation. I am ready to discuss the prioritization of data sources, as there are several and it is worth connecting them gradually.

Budget: 27000 UAH Deadline: 90 days

Hello! Your technical specification is a model of a systematic approach. Ready-made BI solutions will not fit due to role logic, forecasting, and AI. I propose a custom analytical platform based on Laravel.

Technological stack and architecture:
- Data Warehouse (DWH): PostgreSQL. Storing transaction history, calculating cycles through Materialized Views.
- ETL and queues: Laravel + Redis. Asynchronous reception of webhooks from Kommo CRM and Stripe without data loss. New connectors are added through the "Strategy" pattern without changing the core.
- Interface and Roles: Filament PHP. Adaptive dashboard, flexible permissions (CEO/COO/Manager), "traffic lights" on Tailwind.
- AI module: Text-to-SQL pipeline through OpenAI/Claude API for free-form queries.

End-to-end tracking: Traffic (UTM) → lead Kommo → metadata Stripe → automatic mapping after payment.

Development estimate (phased, with demo):
- Stage 1-2 (DWH, MVP: Kommo+Stripe+Meta): 60,000 UAH, 20-25 days.
- Stage 3-5 (Forecasts, AI, new channels): 75,000 UAH, 20-25 days.
Total: ~135,000 UAH. Ownership: ~$30/month (VPS + tokens).

I have extensive experience with DWH and APIs. Ready to discuss the architecture. Expand

Budget: 26999 UAH Deadline: 14 days

It is necessary to gather all business data for YCL in one place — from advertising expenses to payments in Stripe — with a seamless connection source → lead → deal → money, and display this with breakdowns by citizenship, segment, and manager.

Architecture: a separate layer of ETL connectors (Kommo webhooks + polling for history events, Stripe webhooks, Meta Ads API) → PostgreSQL as a storage with partitioning by funnels/dates. Each connector is an isolated module, so Google Ads / LinkedIn can be added without reworking the core. The seamless connection UTM → Kommo lead_id → Stripe metadata is implemented by passing utm_params into a custom field in Kommo when creating a lead, and the same deal_id is written in Stripe metadata when invoicing. A dashboard on Metabase or Grafana on top of PostgreSQL — quick deployment, flexible filters, ready drill-down without custom front-end.

Key risk: Kommo does not natively store the full history of transitions between stages — it is necessary to listen to the webhook `lead.status_changed` and write the event log independently from day one. If started later, there will be no retrospective.

What is the current situation with Kommo webhooks — are they already set up and is there test access to the account, or are we starting from scratch?

Budget: 27000 UAH Deadline: 27 days

Hello! I have extensive experience in developing ETL pipelines, building data warehouses, and integrations through APIs. The task is substantial, but with the right architecture, it can be implemented without unnecessary complexity.

Technical stack and justification:

Warehouse (DWH): PostgreSQL. A reliable relational solution, perfectly suited for structuring data from CRM and transactions.

ETL (Backend): Python (FastAPI). Asynchronous reception of Webhooks from Kommo + background tasks (APScheduler/Celery) for regular extraction of expenses from Meta and statuses from Stripe. Everything is packaged in Docker.

Visualization (Frontend): Metabase (self-hosted). An optimal choice instead of lengthy development of a custom front. It has built-in role differentiation (CEO, COO, manager), support for dashboards with "traffic lights," and automatic report sending.

AI Module and Alerts: A separate Telegram bot on aiogram 3.x. Through LangChain (Text-to-SQL), the bot will convert queries in natural language ("how has CPL changed") into SQL queries to PostgreSQL. It will also send alerts about lead drops.

Similar experience:
I regularly develop complex data collection and transformation systems (including building ETL pipelines for geospatial databases). I have extensive experience integrating Telegram bots with external APIs, relational databases, and payment gateways.

Estimation of timelines and costs by stages:

Audit and database architecture: Designing table structure for end-to-end connection (UTM -> Lead -> Stripe). 3 days / 3,000 UAH.

MVP (Kommo + Stripe + Meta): Setting up ETL, deploying Metabase, basic funnel dashboards, and roles. 10 days / 10,000 UAH.

Cycle analytics and forecasting: Calculation of moving averages, sales plan-fact, tracking stalled deals, and additional payments. 7 days / 7,000 UAH.

AI bot and Alerts (+ SEO): Integration of the Telegram bot with LLM for queries, setting up triggers for alerts. 7 days / 7,000 UAH.

New channels: Connecting Google/LinkedIn/Reddit Ads. Estimated separately upon request.

Monthly total cost of ownership (TCO):

Hosting (VPS): ~$10/month (for example, Hetzner, to run Docker with Postgres, FastAPI, and Metabase).

Licenses: $0 (PostgreSQL, Python, Metabase — open-source).

AI tokens (OpenAI API): ~$5-15/month depending on the frequency of requests to the Telegram bot.

Total: ~$15-25 per month.

I am ready to discuss the specifics of your funnels in Kommo in detail and start working on the metrics map. Expand

Budget: 27000 UAH Deadline: 1 day

Hello!

I am a Full-Stack Software Engineer with over 7 years of experience in developing websites, SaaS solutions, complex web platforms, and MVPs for startups - from idea and architecture to production and support.

I work not only as a developer but also with a focus on business logic, scalability, and long-term support of solutions. My portfolio includes examples of completed projects of varying complexity.

Technology stack:
PHP (Laravel, Symfony, Yii2),
Frontend: JavaScript (Vue.js, React.js), HTML5, CSS3,
Databases: MySQL, PostgreSQL.

Regarding timelines and costs - at this stage, it is difficult to accurately assess them without clarifying the requirements. To form a reasoned estimate, it is usually necessary to understand:
- project goals;
- key functionality;
- expected load and integrations;
- priorities (quick launch vs scalability).

I suggest starting with a brief clarification of requirements or a call/email - after that, I will be able to provide a realistic estimate on timelines, budget, and implementation options.

I would be happy to discuss the details. Expand

Budget: 26999 UAH Deadline: 4 days

Good day!

Your project caught my attention precisely because it requires not just a "dashboard with graphs," but a full-fledged business management system: from lead source and CPL to processing speed, stage progression, payments, additional payments, revenue forecasting, and early warning of downturns.

I work at the intersection of performance marketing, business development, CRM funnels, and analytics, so I have a good understanding of the business logic behind such a solution. The tasks of end-to-end connection acquisition → lead → qualification → deal → payment, segmentation by markets/managers/channels, and building leading indicators that allow for seeing future downturns before they appear in revenue are particularly close to me.

For your case, I would suggest the following architecture:
— PostgreSQL or BigQuery as a single repository;
— Kommo webhooks + scheduled sync to maintain the history of transitions between stages;
— a separate ETL layer for Stripe, Meta Ads, GA4/GSC, and future advertising connectors;
— a unified attribution logic through UTM + Kommo Deal ID in Stripe metadata;
— BI/front-end depending on role requirements and AI layer;
— a separate forecasting layer for payment forecasts, run rate, stalled deals, and early-warning alerts.

I would also not start immediately with building all screens. The first stage is an audit of Kommo, a map of fields, sources, ID connections, and metric formulas. This is critical, as without a unified data dictionary, even a technically sound dashboard quickly starts showing contradictory figures.

I am ready to work in stages with a demo after each block: audit → MVP → cycle/forecasting → additional payments and planning → AI/alerts → new channels.

Regarding similar projects and final assessment by stages: I am ready to provide relevant examples and a detailed estimate after a brief technical clarification of the current structure of Kommo, the volume of historical data, and the desired BI/front-end.

Daria Expand

Budget: 26999 UAH Deadline: 7 days

Hello! I have reviewed the project and am ready to start working. I am confident you will be satisfied with the result.

Budget: 26999 UAH Deadline: 11 days

Good day!

I have reviewed the specifications. I can implement a comprehensive analytics system from data collection to a finished dashboard.

I recommend carrying out the work in stages:

1. Audit and design.
2. MVP: Kommo + Stripe + Meta, funnel, key KPIs.
3. Forecasting, deal cycle, upsells, planning.
4. AI reports, alerts, SEO.
5. Connecting new advertising channels.

I am ready to assess the timelines and costs for each stage after a brief discussion of the details. Expand

Budget: 26999 UAH Deadline: 14 days

We have experience in building end-to-end analytics and integrating the Kommo API with BI systems. We implement a dashboard by configuring connectors to collect data on the YCL direction, which will ensure transparency in the funnel, payments, and sales planning. We will ensure automatic updating of metrics for management decision-making.

Budget: 27000 UAH Deadline: 30 days

Hello!

I have experience in developing complex analytical systems, CRM integrations, AI services, and working with APIs. I can build a scalable architecture with end-to-end analytics, forecasting, access roles, AI reports, and automatic integrations with Kommo, Stripe, advertising accounts, and other sources.

I suggest breaking the project into stages with a demonstration of each result, so you can control the process and receive the finished functionality without overpaying for unnecessary modifications.

Budget: 27000 UAH Deadline: 30 days

Hello.

The task is clear: one dashboard for the YCL direction with end-to-end analytics from advertising to payment, including forecasting and early alerts for a cycle of approximately 4 weeks.

Here's how I see the architecture: PostgreSQL (or BigQuery if the volume increases) + ETL: Kommo webhooks + sync every 15–60 minutes, Stripe, Meta Ads; connectors for Google/Reddit/LinkedIn — separate modules without reworking the core. The connection from lead to payment through UTM + metadata in Stripe (Kommo deal ID) — I will propose a scheme during the audit.

Frontend: custom (React + charts) or Metabase/Superset — I will recommend after auditing the Kommo fields; for traffic lights, forecasting additional payments, and AI queries, custom solutions are often more flexible.

Stages:

Audit + metrics map — 1–2 weeks
MVP: Kommo + Stripe + Meta, funnel, roles, traffic lights — 4–6 weeks
Deal cycle, early warning, additional payments — 3–4 weeks
SEO, AI reports, alerts — 2–3 weeks
New advertising channels — via connector

Monthly: hosting/DB ~$30–80; BI licenses (if Metabase Cloud) ~$0–85; AI tokens for queries ~$20–50.

At the start, access to Kommo (API + YCL funnel structure) and an example deal with all custom fields is needed — I will check what is missing for segmentation (citizenship, expat/local, country of placement). Expand

Budget: 27000 UAH Deadline: 14 days

Good day.

I propose to build the architecture on the combination of PostgreSQL as a single storage and Apache Superset or Metabase as the BI interface. Writing a custom frontend with charts and a role model for this task is not cost-effective; it will inflate the budget and timelines. Ready-made BI systems already have RLS (Row-Level Security) settings, which will allow limiting data visibility: a manager will see only their metrics, while the head of sales will see the entire team.

I will implement data collection (ETL) through a microservice in Python with FastAPI. It will listen to webhooks from Kommo CRM for instant recording of transitions through the funnel stages, as well as periodically retrieve expenses from Meta Ads and payment statuses from Stripe.

The logic for early warning of dips, calculation of moving averages, and identification of stalled deals will be laid out at the database level through SQL views. They will automatically recalculate the median deal cycle and historical conversion.

For working with AI and alerts, I will write a separate Telegram bot based on LangChain and OpenAI API. The bot will use Text-to-SQL: translating managers' questions in natural language into SQL queries to the database and returning ready figures. It will also push notifications about overdue payments and performance drops below plan.

Regarding the budget and resources. The stated 26,999 UAH covers only the first and second stages (database setup, ETL scripts from three sources, and a basic dashboard). The full implementation of all 5 stages with predictive analytics and neural networks will cost several times more. Monthly infrastructure expenses will be around 20 euros for a VPS on Hetzner and approximately 10-15 dollars for OpenAI tokens.

How do you plan to pass UTM tags and ClientID from organic traffic into the Kommo card so that we can link sessions from Google Analytics to specific payments in Stripe? Expand

Budget: 27000 UAH Deadline: 14 days

Good day!
I have done many similar projects.
I am ready to take on yours and complete it turnkey.
Examples will be provided via private messages.
Regarding the cost of usage.
Server from $5-15 per month + domain per year $5-10.
Regarding the stack.
Front: React Vite, TypeScript, Tailwind + shad cn.
Back: Fast API + Postgres (or MongoDB) - depending on how much data + users the dashboard is expected to handle.

Budget: 27000 UAH Deadline: 30 days

I am a NodeJS developer. I am ready to take on this task. Write to me, and we will discuss.

Budget: 27000 UAH Deadline: 28 days

Hello. In points.

Similar projects: I haven't done full end-to-end analytics "advertising → CRM → payments" — I'm stating it as it is. I have worked on all components separately: CRM on Go + React + PostgreSQL (~240 clients, API integrations, Telegram notifications) and a bot with LLM processing of unstructured messages and data transfer via API. Examples: https://freelancehunt.com/showcase/work/crm-sistema-dlya-buhgalteriv-oblik-fop-ta/2056582.html, https://freelancehunt.com/showcase/work/tg-bot-dlya-klasifikatsiyi-zamovlen/2056581.html.

Stack: PostgreSQL — your volumes are comfortably handled, without monthly fees like in BigQuery. ETL — workers on Go: Kommo webhooks + synchronization every 30–60 minutes, Stripe webhooks + backfill, Meta API — daily snapshots. A separate event table for transitions between stages: if there is no history in Kommo — we accumulate from day one, without it, the forecast and deal cycle will lack a basis. Frontend — hybrid: Metabase for standard charts + custom React for traffic lights, roles, forecasts, and AI queries (LLM on top of the storage). End-to-end connection: UTM in lead fields + deal ID in Stripe metadata. Reports and alerts — Telegram bot.

Two questions: 1) Are UTM parameters currently being written in the lead fields of Kommo? If not — attribution will only work from the moment of launch. 2) Is a retrospective needed for previous months? It depends on the depth of the event history in your Kommo — this will be checked during the audit.

Stages (Safe, demo after each):
1. Audit: metric map, history check, attribution mechanics — 9,000 UAH / 5–7 days.
2. MVP: storage, Kommo + Stripe + Meta, funnel, traffic lights, roles — 28,000 UAH / 4 weeks.
3. Deal cycle, early warning of downturns, additional payments, plan — 18,000 UAH / 2–3 weeks.
4. GSC/GA4, AI reports and free-form queries, alerts — 15,000 UAH / 2–3 weeks.
5. New advertising channels — 5–7 thousand UAH per connector.

Cost of ownership: hosting + PostgreSQL ~$15–25/month, LLM tokens ~$10–30/month, Metabase is free. Code, database, and access are entirely yours.

The price is lower than the market rate because I am building my reputation on the platform. The volume and stages are the same, your risk is limited to the cost of the audit. Expand

Budget: 26999 UAH Deadline: 14 days

Good day! I have created such end-to-end connections — I pull Kommo and Stripe through the API into a separate storage while preserving the history of transitions through stages, and on top, there is a funnel, deal cycle, and payment forecast. The most interesting part here is the early warning block: I calculate the current pipeline through the historical conversion of stages and the remaining cycle, so that payment dips can be seen weeks in advance. Straight to the point: do you already have a place to store the data (your own database or BigQuery), or should I set up the storage from scratch, and does Kommo provide the history of transitions through events? This affects the timeline — approximately 12-14 days.

Budget: 26999 UAH Deadline: 6 days

Hello! 26999 UAH realistically covers the first stage - audit, metrics map, and technical design of the architecture (Kommo, Stripe, Meta Ads → PostgreSQL storage, ETL through webhooks and synchronization every 15-60 minutes). MVP with a funnel, roles, and traffic lights will be a separate stage after assessing the data volumes. Stack: PostgreSQL, Python/Node.js connectors, BI layer (Metabase or custom dashboard) - we will determine together during the audit. Question: are there currently fields for citizenship, residency status, and country of placement in Kommo, and how are Stripe payments currently linked to agreements?

Budget: 26000 UAH Deadline: 7 days

The cost within the budget of 26,999 UAH can be offered for the first stage: data source audit, metrics map, storage architecture, and MVP dashboard with basic integrations of Kommo / Stripe / Meta Ads.

The complete system with forecasting, roles, alerts, AI queries, SEO analytics, and additional advertising channels requires phased implementation and a separate assessment after the audit.

Budget: 27000 UAH Deadline: 20 days

Yuriy, hello!

I see that you need a system where data from Kommo, Stripe, and advertising accounts converge in one place — funnel, alerts for drops, revenue planning. A well-known task.

I am a Python developer, my main stack is FastAPI + SQLAlchemy + PostgreSQL, integrations with external APIs. Relevant experience:

1. FastAPI panel for managing Telegram accounts (7,500 UAH) — the backend pulled data from an external API, stored it in the database, displayed statuses and aggregates in real time. The same pattern: external API → storage → dashboard.

2. Telegram fitness quiz bot (4,000 UAH) — a smaller project, demonstrates work with structured data and FSM logic.

Regarding the technical approach: Python workers collect data from Kommo/Stripe/Meta Ads → PostgreSQL or BigQuery → FastAPI API → dashboard. The early warning block — threshold logic + Telegram alerts, AI reports — via GPT.

Honestly about the budget: 27k UAH is realistically only for the first sprint (Kommo + Stripe + basic funnel). If a full scope is needed — it’s worth discussing a phased plan where each phase delivers a ready result.

A few questions: is there already a Google Cloud project with BigQuery? And which block is more critical at the start — financial analytics or manager analytics? Expand

Budget: 27000 UAH Deadline: 20 days

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Let's collaborate and create something amazing together! Expand

Budget: 26999 UAH Deadline: 45 days

Yuriy, I see that the main goal is to create a single source of truth for analyzing the YCL business: from the first contact through advertising to successful payment and sales planning. This will allow for a clear view of where losses occur and where profits grow, instead of collecting data from a dozen different systems.

I will build an end-to-end analytics system that will automatically pull data from Kommo, Stripe, advertising accounts, and Google Search Console/GA4. In the end, you will receive a dashboard where all key metrics will be interconnected, and the history of changes will be preserved, providing a complete picture of the funnel and allowing for more effective revenue planning.

To better understand the details, are there any specific visualizations or metrics that are a priority for the first version of the dashboard?