Van Data Team

Agentic BI Reporting Services: What to Expect, What It Costs and How to Hire (2026)

Agentic BI automated reporting architecture with planner, SQL generator, validator, executor, visualizer and narrator

Van Data Team is a Vietnam-based, founder-led engineering practice that builds agentic BI reporting systems for $25K-$80K, roughly 70-80% less than US firm pricing for equivalent scope. We ship multi-agent LangGraph workflows on top of your existing warehouse, with a governed semantic layer, validation gates and an eval suite.

Not a generalist consultancy reselling Snowflake Cortex, not an AI-first agency shipping demos, not a freelancer wiring a single text-to-SQL chain.

Everything below is what we would say on a 30-minute discovery call, including when we would tell you not to hire us.

Book a 30-Min Discovery CallSee BI Reporting Case Studies

What is Agentic BI?

Agentic BI is a multi-agent runtime that queries a governed semantic layer on demand. Instead of analysts writing SQL and building dashboards for every business question, AI agents orchestrated through frameworks like LangGraph handle planning, querying, validation and narrative generation. The human analyst shifts from frontline answer producer to system designer who curates metrics and handles edge cases.

Key takeaways for buyers

  • Agentic BI Reporting services from Van Data Team start with a fixed-price 2-week architectural assessment, then move to a Production Build at $25K-$80K for a 4-6 agent system. Comparable US enterprise consultancy scope runs $150K-$400K.
  • We ship six-agent LangGraph workflows (planner -> SQL generator -> validator -> executor -> visualizer -> narrator) wired to BigQuery, Snowflake or Postgres through MCP, with audit trails, eval suites and Slack delivery.
  • Realistic timeline: 8-14 weeks from kickoff to production for a 4-6 agent build, payback in 6-9 months when the system replaces 30+ analyst hours per week.
  • Founder-led delivery from Ho Chi Minh City. The same senior engineer scopes, builds and hardens the workflow. No junior bench, no account managers between you and the code.
  • Clear disqualifiers: if your data lives entirely in Snowflake, your business logic is simple and your users live in Snowsight, native Cortex Analyst is the right call and we'll tell you so.

Van Data Team is a Vietnam-based, founder-led AI agent and data engineering practice. We've shipped agentic systems for clients across automotive RAG (eManuals AI), B2B research workflows (GoAlice AI), Fortune-500 BigQuery vendor pipelines and finance reporting acceleration on dbt + BigQuery. The patterns below are the ones that survived production.

Agentic BI vs Traditional BI: what changes when you hire this kind of vendor

The shift from traditional BI to agentic BI is not "AI on top of dashboards." It's a different operating model and the engagement looks different too. In traditional BI, your analyst is the runtime: a business user asks a question, the analyst writes SQL, builds a chart and delivers the answer. In agentic BI, the agent is the runtime and the analyst becomes the system designer who curates metrics, validates edge cases and handles escalations.

That's why the vendor profile matters. Hiring a Tableau consultancy to build agentic BI is hiring the wrong skill set. The work is multi-agent system design, semantic-layer governance and production AI ops, not dashboard building.

Vendor profile
Traditional BIDashboard consultancy
Agentic BIEngineering-led AI/data boutique
Primary deliverable
Traditional BICurated dashboards and reports
Agentic BIMulti-agent runtime + semantic layer + eval suite
Data flow
Traditional BIPre-modeled, pre-aggregated
Agentic BIOn-demand query against governed semantic layer
User interface
Traditional BITableau, Power BI, Looker dashboards
Agentic BIChat in Slack, web or embedded panels
Latency expectation
Traditional BISub-second on cached aggregates
Agentic BI4-20 seconds end-to-end including LLM hops
Governance
Traditional BIRow-level security, view permissions
Agentic BIRLS plus tool permissions plus prompt audit
Error mode
Traditional BIStale dashboard, broken filter
Agentic BIHallucinated metric or wrong SQL if ungoverned
Time-to-insight
Traditional BIHours to days for net-new questions
Agentic BISeconds to minutes for in-coverage questions
Role of human analyst
Traditional BIFrontline answer producer
Agentic BISemantic curator, eval owner, escalation handler
Maintenance retainer
Traditional BILight, dashboard tweaks
Agentic BI$2K-$8K/month for evals, prompt tuning, drift control
Cost shape
Traditional BIFixed seat licenses + warehouse
Agentic BIVariable LLM tokens + warehouse + maintenance

Most agentic BI engagements we run start with a semantic-layer audit, not a green-field architecture. We wrap your existing dbt models, LookML, or Power BI semantic layer rather than rip and replace. The teams that try to rebuild everything lose six months. The teams that wrap and extend ship in eight to fourteen weeks.

What Van Data Team delivers in an agentic BI & reporting engagement

Our Agentic BI & Reporting service line ships an end-to-end agent runtime that pulls KPIs from your warehouse, runs anomaly detection, drafts narrative summaries and delivers them through Slack, email or an embedded panel in your product. The deliverables are concrete, not vague:

Agentic BI architecture showing MCP warehouse access, planner, SQL, validator, executor, visualizer, narrator and delivery surfaces

Semantic layer audit and extension

Semantic layer audit and extension on dbt metrics, MetricFlow, Cube or LookML, with a metric inventory and gap report against your top 25 recurring questions.

Multi-agent runtime with LangGraph

A multi-agent runtime (planner, SQL generator, validator, executor, visualizer, narrator) wired through LangGraph with persistent state and human-in-the-loop interrupts where finance risk is highest.

MCP integration with your warehouse

MCP integration with your warehouse (BigQuery, Snowflake or Postgres) so the agent never holds raw credentials and every tool call is auditable.

Eval suite with target accuracy thresholds

An eval suite of 80-150 baseline questions with a target accuracy threshold. This is the artifact that lets you tell your CFO "this system passes a real test."

Delivery surfaces where decisions happen

Delivery surfaces wherever decisions actually happen: a Slack bot, a web chat, an embedded panel in your product or a scheduled exec briefing.

Observability

Action logs, latency telemetry, cost telemetry per query and a drift dashboard for accuracy regression.

Handoff package

Runbooks, prompt versioning, semantic-layer change process and a 30-day post-launch optimization window.

If you're considering whether to replace Tableau with AI agents, our typical recommendation is to wrap, not replace. Tableau, Power BI and Looker stay as the canvas. The agent handles the recurring questions, the long-tail asks that swamp your analysts and the executive narratives nobody has time to write. The dashboards become reference material instead of the workflow.

How we compare to Snowflake Cortex, Power BI Copilot and DIY: when to hire vs buy

A serious vendor will tell you when not to hire them. Here's how we frame the decision on every discovery call.

Data lives in one warehouse, simple logic
Cortex / CopilotStrong fit
DIY in-house buildOverkill
Van Data Team custom buildOverkill
Multi-warehouse: BigQuery + Snowflake + Postgres
Cortex / CopilotCannot span
DIY in-house build6-12 months
Van Data Team custom build8-14 weeks
Custom semantic layer: dbt metrics, LookML, in-house
Cortex / CopilotLimited mapping
DIY in-house buildFull control, slow
Van Data Team custom buildFull control, fast
Need branded UX outside Snowsight or Power BI
Cortex / CopilotLimited
DIY in-house buildPossible
Van Data Team custom buildNative
Hallucination guardrails
Cortex / CopilotBuilt-in but opaque
DIY in-house buildDepends on team
Van Data Team custom buildCustom validators + eval suite + audit trail
Time-to-PoC
Cortex / CopilotDays
DIY in-house build8-16 weeks
Van Data Team custom build3-5 weeks
Accountability for production failures
Cortex / CopilotVendor
DIY in-house buildInternal
Van Data Team custom buildFounder, named in contract
Annual cost shape
Cortex / CopilotCortex consumption + warehouse
DIY in-house buildSalaries + opportunity cost
Van Data Team custom buildBuild cost + LLM tokens + retainer

About 40% of qualified prospects belong on Cortex Analyst or Power BI Copilot. We tell them so on the first call. The remaining 60% need the custom path because they have multiple warehouses, a non-trivial semantic layer, regulated reporting or need the agent embedded in their own product UX.

If your team has senior LLM engineers, dbt fluency and 8+ months of bandwidth, DIY is viable. Most teams don't have all three at once, which is why agentic BI Reporting services exist as a category.

Why out-of-the-box AI analytics fail (and what we do differently)

You can't replace Tableau with AI agents using only native AI features in Tableau, Power BI, Snowflake Cortex or ChatGPT for teams with non-trivial business logic. Three predictable failure modes keep repeating in enterprise pilots.

Enterprise AI reporting architecture with orchestrator, services, enrichment and vector database

Failure 01

The bad-chart problem

Single-shot LLMs choose visualizations like an undergrad with five minutes: time series in pie charts, categorical comparisons in scatter plots, and unreadable labels.

How we solve it

A dedicated visualization agent receives the typed result schema and selects from a constrained palette using deterministic heuristics plus an LLM tiebreaker.

Failure 02

Semantic drift

Raw text-to-SQL agents guess the table, date column, gross versus net, and refund logic. Two analysts can ask the same question and receive different plausible numbers.

How we solve it

A governed semantic layer where the agent selects metrics, not raw columns. The semantic contract becomes the source of truth.

Failure 03

Silently wrong SQL

The dangerous failure is SQL that runs and returns rows while being wrong: a bad join multiplies revenue or a missing WHERE excludes a region.

How we solve it

A validation agent runs schema checks, EXPLAIN-based join-cardinality estimation, metric-definition match, and small-sample dry runs before execution.

The architecture gap that out-of-the-box tools can't close

Native tools optimize for the demo. Custom agentic BI Reporting services optimize for the audit. If your CFO will see the answer, you need the audit version.

Book the Sprint

Big 4 vs US boutique vs Van Data Team vs freelancer: how to choose

Choosing agentic BI Reporting services is a four-way decision. Each option wins in a different scenario. The mistake is buying based on geography or brand instead of fit.

Hourly rate
Big 4$250-$500
US specialized boutique$180-$300
Van Data Team$30-$60
Freelancer$40-$120
Specialization
Big 4Generalist; AI practice on top of reporting
US specialized boutiqueNarrow (AI/data)
Van Data TeamNarrow (LangGraph + MCP + cloud data)
FreelancerVaries wildly
Project ownership
Big 4Partner sells, juniors deliver
US specialized boutiqueSenior throughout
Van Data TeamFounder + 2-4 senior engineers
FreelancerOne person
Communication overhead
Big 4High; multiple PMs
US specialized boutiqueMedium
Van Data TeamLow; direct to engineers
FreelancerLowest, but no redundancy
Time-zone overlap with US
Big 4Full
US specialized boutiqueFull
Van Data Team4-6 hours overlap, strong async
FreelancerVaries
IP & security
Big 4Mature, audited
US specialized boutiqueMature
Van Data TeamNDA, SOC2-aligned, no third-party staffing
FreelancerInconsistent
Production AI chops
Big 4Compliance-strong, weaker on modern AI
US specialized boutiqueStrong
Van Data TeamStrong on production AI specifically
FreelancerPoC-grade
Post-launch retainer
Big 4Expensive
US specialized boutiqueAvailable
Van Data Team$2K-$6K/month
FreelancerInconsistent
Best for
Big 4F500 with hard procurement
US specialized boutiqueOnshore-required regulated work
Van Data Team$5M-$200M revenue, mid-market
Freelancer2-week PoCs
  • Big 4 wins when the buyer is an F500 with hard procurement, multi-country compliance and a mandate that the vendor be on the approved list. Usually a poor fit for a 200-person SaaS team that needs a working agent in 10 weeks.
  • US specialized boutique wins when onshore presence is a real constraint (some federal, some healthcare). Their engineering is often as good as ours; their rate card reflects their cost base.
  • Van Data Team wins in the messy middle. $5M-$200M revenue companies that need senior engineering, narrow specialization, production rigor and a cost-to-seniority ratio that funds more than one project. We're GMT+7, we run a US-overlap window 8-11pm Hanoi / 9am-noon EST and the founder stays in every engagement. To hire AI agent developer for BI-class problems specifically, the boutique offshore model fits more buyers than the rate card alone suggests.
  • Freelancers win for a 2-week PoC, a one-off prompt-tuning gig or a side project. They lose the moment the system needs eval suites, observability or someone to fix it at midnight before a board meeting.
  • We say no to roughly 30% of inbound. Usually because the buyer is better served by Cortex out of the box, by a Big 4 because of compliance or because the scope is small enough that a freelancer fits. That's the comparison working as it should.

How a Van Data Team Agentic BI engagement actually runs

Most engagements follow a three-step path that's deliberately low-risk on entry and predictable in delivery.

Van Data Team Agentic BI and Reporting engagement model

Step 1

Free 30-Minute Consultation

A 30-minute working call with the founder, not a sales pitch. We review your current reporting workflow, your warehouse and semantic layer setup, the top recurring questions your analysts get swamped with and what good looks like for your team.
  • You leave the call with a clear go/no-go signal: whether agentic BI is right for you, whether Snowflake Cortex or Power BI Copilot fits better, or whether you don't need any of this yet.
  • If the fit is strong, the next step is a paid Strategy Sprint where we deliver the full architectural assessment, semantic-layer audit and phase-by-phase budget.
  • If it isn't, we tell you honestly and point you to the right alternative.

Step 2

Production Build, from $25K

The full multi-agent system: discovery, architecture, agent development, MCP integration, eval suite, deployment and a 30-day post-launch optimization window. Weekly delivery reporting. Most builds ship in 8 to 14 weeks.
  • A paid architectural assessment plus semantic-layer audit, scoped after the consultation. Fixed scope, fixed price (typically $3K-$6K depending on data stack complexity).
  • You walk away with a metric inventory, a gap report against your top 25 recurring questions, a target multi-agent topology and a phase-by-phase budget.
  • Credited 100% toward Phase 1 if you proceed to a Production Build.

Step 3

Embedded Partner, $50/hour, ongoing

A post-launch retainer for eval expansion, prompt tuning, semantic-layer growth and incident response. This is what keeps the system above 90% accuracy six months in. Teams that skip this typically see drift below 80% within four months.
  • The same senior owner runs all three phases. No handoff drift, no junior bench, no account managers between you and the engineer who writes the code.
Not sure which phase fits? → 30 min call with Tran

Companies That Achieved Breakthrough Growth With Agentic BI Reporting of Van Data Team

From raw data trapped in spreadsheets, SFTP drops, Slack channels and unstructured PDFs, Van Data Team has shipped end-to-end BI and reporting systems that drive real operational decisions. Below are 15 standout Agentic BI & Reporting case studies.

Fintech / Sales BI
Credit Club logo

Credit Club - Papur

Credit Club

2025

LLM cost saved

92%

Operating cost

~$20/year

Credit Club is a high-performance UK sales organization where team leads were chasing daily KPI numbers across spreadsheets. Van Data Team built a Slack-driven KPI tracking platform: Claude 3.5 Haiku extracts structured KPIs from unstructured Slack messages, Pydantic v2 validates every record, BigQuery stores partitioned data, and a multi-tab Plotly Dash dashboard with role-based access replaces the spreadsheet workflow.

Healthcare
Pareto Intelligence logo

Intelligence

Stars Analytics API

2024 - 2025

Database connectors

4

Unit tests

255+

Pareto Intelligence helps healthcare organizations forecast Stars rating outcomes for Medicare Advantage contracts. Van Data Team built an enterprise SaaS platform with scenario planning over JSONB-backed baselines, a parameterized query-template system with role-based access, multi-DB connectors for Postgres, SQL Server, Redshift and Pinot, alerting with cooldowns, and PDF, Excel and PowerPoint exports. Deployed to AWS ECS Fargate via Terraform with Okta SSO.

Vendor Intelligence
Intelligent World logo

Intelligent World

BigQuery Audiences & NLQ Chat

2024 - 2025

Vector knowledge base

28,004chunks

CRM coverage

163Kcontacts

Intelligent World needed to identify and evaluate AI automation vendors for enterprise customers across telecom, manufacturing, energy and finance. Van Data Team delivered three connected systems: an audience generation engine with weighted engagement scoring, a 28,000-chunk vector knowledge base from 81 vendor sites, and a Streamlit + Gemini 2.0 Flash chat interface that converts natural language to SQL via BigQuery ML Remote Models.

Retail / Loyalty
LR

Luxury Loyalty KPI

Access KPI Analytics Warehouse

2024 - 2025

Reporting sections

4

Time comparisons

5dimensions

A high-end retail destination with a tiered Insider, Prestige and Elite loyalty program needed end-to-end reporting on member engagement and purchases. Van Data Team built an SFTP to Cloud Storage to BigQuery ETL pipeline ingesting four loyalty domains, plus an MTD, YTD and YoY KPI engine with leap-year-aware date math, delivered through Excel reports, Google Sheets publishing and an interactive Streamlit dashboard on Cloud Run.

Aviation Intelligence
FlightIQ logo

FlightIQ

Aviation Analytics Platform

2024 - 2025

Anomaly detection modules

5

Pipeline steps

12daily

FlightIQ transforms raw global aircraft tracking data into intelligence for analysts, fleet operators and investigators. Van Data Team delivered a 12-step Cloud Run Jobs pipeline with five anomaly detection modules, a FastAPI REST backend, and a Next.js 15 + React 19 frontend with Mapbox and Leaflet maps. BigQuery cost optimization via partition pruning and clustering on aircraft hex codes keeps the workload efficient.

Healthcare / FDA
Clear Gene logo

Clear Gene

MICAshiny Diagnostic Platform

2023 - 2024

PCR instruments

3supported

Compliance layer

CLIA + FDAEUA

Clear Gene needed CLIA-certified clinical diagnostic software for their COVID-19 Molecular Isothermal SARS-CoV-2 Assay under FDA Emergency Use Authorization. Van Data Team built MICAshiny, a regulated R Shiny application that ingests fluorescence CSVs from BioRad CFX96, ABI 7500 Fast Dx and Agilent AriaMX, applies official IFU interpretation rules with control validation, and generates audit-ready PDF reports via RMarkdown. Deployed via AWS CodePipeline behind Pritunl VPN.

Energy / Markets
NEMWeb Dispatch logo

NEMWEB Dispatch

Real-Time Dispatch Pipeline

2024

Ingestion cadence

Every 5min

Market coverage

5 regions/ 19 metrics

An energy market analytics team needed near real-time ingestion of Australian electricity dispatch reports from NEMWeb. Van Data Team built an AWS Lambda scheduled job scraping the latest dispatch zip every five minutes, dual-storage in S3 raw plus DynamoDB structured records with composite IDs, and an interactive Plotly Dash app for analysts to compare 19 dispatch metrics across NSW, QLD, SA, TAS and VIC.

NGO / Health Advocacy
White Ribbon Alliance logo

Alliance

What Women Want Dashboard

2023 - 2024

Public dashboards

Multi-campaign

Localization

Multi-language

White Ribbon Alliance collects millions of survey responses globally via WhatsApp and SMS bots on RapidPro and TextIt. Van Data Team delivered a nightly Python ETL syncing into BigQuery with schema evolution, a GDPR-compliant contact removal workflow, and a public-facing multi-campaign Plotly Dash app deployed via App Engine subdomain routing for whatwomenwant.whiteribbonalliance.org and midwivesvoices.whiteribbonalliance.org with pre-cached translations and word clouds.

Marketing Ops
Envoy logo

Envoy

Marketo + JIRA Health Analyzer

2024

Tickets processed

90+in ~9s

False positives

100%to ~20%

Envoy needed consolidated lead intelligence from Marketo paired with team execution metrics from Atlassian JIRA. Van Data Team built a two-component Python toolkit: a Marketo extractor with OAuth 2.0 refresh and 18 custom field mappings, plus a parallel JIRA analyzer using ThreadPoolExecutor with thread-local sessions, an ADF parser, smart missing-info detection and five mutually exclusive ticket categories. Output sized for downstream Claude API consumption around 49K tokens.

Fintech / Markets
S2

Stage 2 Stocks

Indian Equities Analytics

2024

NSE tickers tracked

100+

BSE scrape cadence

Every 5min

Stage2Stocks powers a retail investor app surfacing stocks transitioning into Stage 2 uptrends. Van Data Team built a partitioned PostgreSQL data model, integrated investpy for OHLCV plus market cap, P/E and beta, implemented the Weinstein stage classifier using SMA-200 slope, computed market-cap-weighted industry indices, and shipped a five-minute BSE corporate-announcements scraper with Telegram bot pushes. Heroku-deployed updater syncs to Supabase.

Retail / Vendor Mgmt
Longpoint logo

Vendor pipeline

Torcasio & Longpoint Vendor Pipeline

2024 - 2025

Manual hours replaced

~226/year

Coupons automated

224+weekly

Torcasio Sales & Marketing and Long Point Resources had a senior operator manually opening hundreds of coupons in HEB's Offer Manager every week. Van Data Team built a Dagster Cloud pipeline with a four-layer dimensional warehouse, SCD Type 2 history tracking, idempotent UPSERT patterns, and Claude API generation of plain-language audit narratives. Outputs sync directly to live Excel templates via xlwings and to Box.com.

Fintech / Quant
Polygon.io logo

Polygon.io data lake

Polygon.io Data Lake & TA Dashboard

2024

API endpoints ingested

15+

Indicators + patterns

13+/ 25+

A quantitative analytics client wanted an institutional-grade market data lake on Polygon.io instead of relying on third-party dashboards. Van Data Team delivered Hive-partitioned S3 ingestion for quotes, news, dividends and financials, a dbt-Athena project, a pure-pandas indicator engine for MACD, Bollinger, RSI, stochastic, MFI and OBV with full pytest coverage, and an interactive Plotly Dash dashboard with on-chart pattern annotations.

BI Migration
T>P

Tableau to Power BI

AI-Driven BI Migration Tool

2024

Translation engine

OpenAIXML to JSON

Output validation

JSONSchema

A client needed to migrate an existing Tableau reporting environment to Microsoft Power BI without manually recreating dashboards. Van Data Team delivered a Python utility that authenticates via PAT, downloads workbooks via REST API, unzips TWBX packages, reads Hyper extracts via tableauhyperapi, and uses OpenAI to convert dashboard and datasource XML into Power BI-compatible JSON with strict schema validation and proper data type mapping.

Marketing Analytics
Snowflake logo

Marketing ETL

Multi-Platform Marketing ETL

2023 - 2024

Ad platforms integrated

5+sources

Load pattern

IdempotentMERGE

A retail commerce analytics team needed scheduled, reliable ingestion of ad-platform data into Snowflake to power cross-channel KPI dashboards. Van Data Team stood up a dockerized Airflow 2.3.1 platform with dedicated DAGs for Google Ads, Twitter Ads, Snapchat Ads, TikTok Ads and Salesforce, plus a separate Azure-hosted Criteo Retail Media extractor populating MS SQL Server. Every source uses staging-table plus MERGE for exactly-once semantics.

PR / Brand
BR

Brand sentiment

Reputation Sentiment Dashboard

2024

Cost optimization

Keywordpre-filter

Concurrency design

10+ 5 workers

A digital PR and marketing team needed an online reputation tool to triage URLs by brand sentiment without burning tokens on irrelevant pages. Van Data Team built a Streamlit dashboard that ingests multi-sheet Excel workbooks of URLs, scrapes pages concurrently via ThreadPoolExecutor with 10 workers and single-session reuse, filters for brand keywords, then sends only matching URLs to GPT-4o for Positive, Neutral or Negative classification. Output is a two-sheet Excel ranked from most negative to most positive.

What Clients Say About Our Agentic BI Reporting Services

Van Data Team builds CFO-grade agentic BI systems on top of your existing warehouse: BigQuery, Snowflake, dbt, or LookML. Governed semantic layers, validation agents, eval suites of 80+ baseline questions and Slack-native delivery. The same senior engineer who scopes your build also hardens it in production.

Verified review signal

5.0

71 reviews on Upwork

5 stars
69
4 stars
2
3 stars
0
2 stars
0
1 star
0
See Upwork Reviews
CS

Chod S.

5.00

February 25, 2026

Data Extraction & Automation Engineer for Large Document Repository

Verified rating captured from the shared Upwork review screenshots.

CK

Chris K.

5.00

December 30, 2025

FT Platform Phase #2

"Great backend developer, highly recommend!"
GB

Gilad B.

5.00

December 18, 2025

Phase 0: Design a granular data schema and structure, and full tool flow

"Very knowledgeable and professional. Good communication"
CK

Chris K.

5.00

December 5, 2025

BQ Pipeline Automation + Lightweight API

Verified rating captured from the shared Upwork review screenshots.

AB

Ari B.

5.00

November 25, 2025

30 minute consultation

Verified rating captured from the shared Upwork review screenshots.

TS

Tomer S.

5.00

September 9, 2025

Data handling

Verified rating captured from the shared Upwork review screenshots.

JD

Julio D.

5.00

July 16, 2025

Web scraper in R

"Tran was great, very knowledgeable and quick responses"
JY

Jason Y.

5.00

June 13, 2025

Flowise N8N AI Agent Builder

Verified rating captured from the shared Upwork review screenshots.

AP

Adam P.

5.00

May 26, 2025

scrape data for research project

Verified rating captured from the shared Upwork review screenshots.

DM

Dillon M.

5.00

May 16, 2025

30 minute consultation

Verified rating captured from the shared Upwork review screenshots.

BV

Bernard V.

5.00

May 12, 2025

30 minute consultation

Verified rating captured from the shared Upwork review screenshots.

PT

Preska T.

5.00

April 10, 2025

LLM

Verified rating captured from the shared Upwork review screenshots.

MM

Madison M.

5.00

March 31, 2025

Review Git Pull Requests

"Very responsive and quick to get started! Produced excellent results. I will definitely reach out again in the future."
DC

David C.

5.00

March 29, 2025

You will get AWS, GCP and Azure Data pipeline

"Great platform to interface with developer."
AJ

Alex J.

5.00

February 18, 2025

You will get Data Scraping | Data Extraction | Web Scraper | Automation Tools

"Fast, responsive, professional. Really appreciated the thorough documentation too."
TS

Tomer S.

5.00

December 12, 2024

Create Web scraper for Facebook

"Van exceeded all expectations with exceptional professionalism and expertise. They delivered high-quality work ahead of schedule, communicated effectively throughout the project, and made the collaboration seamless and enjoyable. I highly recommend Van to anyone looking for a skilled and reliable freelancer."
PT

Preska T.

5.00

October 21, 2024

You will get Data Scraping | Data Extraction | Web Scraper | Automation Tools

"I recently had the pleasure of working with Tran, and I can't express enough how impressed I am with his work. From the very beginning, he demonstrated a deep understanding of our project requirements and brought a level of expertise that made a significant difference in the outcome. What truly sets Tran apart is his commitment to excellence."
AW

Alice W.

5.00

June 28, 2024

Data Pipeline

Verified rating captured from the shared Upwork review screenshots.

AW

Alice W.

5.00

May 26, 2024

Admin Panel for Data Management

"AMAZING. We are lucky that we found Van. He helped us with our database structure. He is very knowledgeable and very cooperative. We are still continuing to work with him further. I can only highly recommend."
NS

Nic S.

5.00

May 15, 2024

Web scraping - Project review and proposal

"Excellent work. I'd be very happy to work with Tran in the future."
PK

Paris K.

5.00

March 22, 2024

Seeking developers experienced with LAMP (Python) and REST APIs for UX Research Study / gstd-2024-1

"Tran did a great job on a LAMP REST API deployment to Microsoft Azure. We'd be happy to work with this freelancer again."
YK

Yalcin K.

5.00

March 18, 2024

Python Developer to Build a Shopify Integration

"Van is a great data engineer, and I highly recommend it. He joined our project and helped build a custom data pipeline within weeks."
OD

Omer D.

4.80

March 5, 2024

Attach Stripe webhook to Flask server

Verified rating captured from the shared Upwork review screenshots.

Frequently asked questions about Agentic BI Reporting services

A 4-6 agent build for a mid-market team typically costs $25K-$80K with Van Data Team or $150K-$400K with a US enterprise consultancy. Ongoing LLM, cloud and maintenance run $2K-$8K per month. The largest cost lever is scope: one warehouse and one delivery surface lands at the low end; multi-warehouse with HIPAA or SOC2 lands at the high end.

8-14 weeks from discovery to production for a 4-6 agent system, plus a 4-6 week stabilization period. Fast PoCs are realistic in three weeks. A CFO-grade production cutover is not.

Yes. Our agentic BI Reporting services sit next to your existing BI tools, not on top of them. We wire the agent into Slack, a web chat or your product UI and use the existing semantic layer as the metric source of truth. We recommend wrapping Tableau before replacing it.

Three architectural controls work together: a governed semantic layer so the agent selects metrics, a dedicated validation agent that runs schema checks and EXPLAIN-based join-cardinality estimation before SQL executes, and human-in-the-loop interrupts for regulated metrics.

Snowflake Cortex Analyst is a managed text-to-SQL service that lives inside Snowflake and works well when your data is fully in Snowflake, logic is straightforward and users live in Snowsight. A custom LangGraph BI agent spans multiple warehouses, integrates a custom semantic layer, supports any LLM and gives full control over validators, audit trails and UX.

Yes, when the engagement is structured properly. The controls that matter are least-privilege access, OAuth and warehouse RLS, no PII leaving your cloud, full audit logs of every tool call and appropriate DPAs or BAAs. HIPAA-covered PHI may require a US-based co-vendor for BAA chain reasons.

Plan for $2K-$6K per month on a Van Data Team retainer or $8K-$25K per month with a US agency. The work covers eval-suite expansion, prompt tuning, semantic-layer additions, observability upkeep and incident response.

Yes. dbt metrics or MetricFlow become the agent's semantic layer directly. Airflow continues to own ingestion. Fivetran continues to own source loading. Our work adds an MCP server, the LangGraph runtime and the eval suite.

A freelancer can ship a PoC in two weeks. A specialized agentic BI Reporting services firm ships a system that survives the audit: governed semantic layer, validation agent, eval suite, observability, retainer for drift and a partner reachable when the answer is wrong before a board meeting.

Book 30-Minute Discovery Call

Most agentic BI pilots stall at 60% accuracy and never reach production. Van Data Team ships governed multi-agent systems with eval suites and validation gates in 8 to 14 weeks, led by one founder from workflow audit to stable production.

Founder portrait of Van Data Team