AI Agent Development Services
Stop burning hours on repetitive questions, manual reconciliations, and copy-paste content workflows.
Deploy custom AI agents that read your real data and act across BI, ERP, and CMS, with humans approving the high-impact moves. Built with LangGraph, Google Gemini, OpenClaw, Anthropic Claude, and OpenAI. Fast delivery, flexible pricing, and the right mix of frameworks for the job.
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AI Agent Services We Offer
Conversational Analytics Agents
Ask your data questions in plain language and get answers backed by your real numbers. We build agents that translate questions into governed SQL or semantic-layer queries against BigQuery, Snowflake, Power BI datasets, or your warehouse, and return charts, tables, and short narratives your team can trust.
AI Content Operations Agents
Closed loops between Search Console, GA4, operational data, and your CMS. The agent reads what is performing, decides what to publish next, drafts pages, attaches AI-generated hero imagery, and pushes everything to a separate branch for human review before going live.
Process Automation Agents
Predictable, multi-step automations for supply chain, retail, and operations workflows. Reconcile orders across ERP, WMS, and TMS. Match invoices to purchase orders. Route exceptions to the right humans. Built on LangGraph for state, retries, and audit-friendly execution.
Internal Copilots over BI and Operational Systems
Domain copilots for merchandisers, demand planners, fleet managers, and finance teams. They answer recurring questions, surface anomalies, and prepare draft actions in CRMs, ERPs, or BI tools, leaving the final decision and click to a human.
Document and Report Intelligence
Extract structured data from PDFs, scanned forms, proofs of delivery, lab reports, and contracts. Combine OCR, vision models, and reasoning agents to populate your databases and dashboards without manual re-keying.
Autonomous Research and Monitoring Agents
Agents that monitor competitor sites, marketplaces, regulations, or operational signals. They run on a schedule, summarize what changed, and only escalate to humans when thresholds are crossed, with every step logged and reproducible.
How a Witanalytica AI agent runs
Every step is observable. Every action passes a guardrail. Humans stay in the loop where it matters.
Data sources
GA4, GSC, BigQuery, Snowflake, ERP, CRM
Agent reasoning
LangGraph or Vertex Agent Engine
Human approval
Slack, email, or review UI
Action
Publish, alert, write back, ticket
The output of every action feeds back into the data layer. The loop closes, and the next run is smarter.
Representative Workflows
Healthcare network: AI content operations under human review
A multi-clinic healthcare network where we deployed a daily agent that reads Search Console queries, GA4 user behavior, and internal practitioner availability data. It produces a short summary, decides which new pages would best match patient demand, drafts the pages, generates hero imagery with GPT Image 2, and pushes everything to a separate branch. A human editor reviews and approves before publishing. Currently operating, results being measured.
Logistics and retail: process automation with deterministic steps
A reference architecture for 3PLs, distributors, and multi-warehouse retailers. A LangGraph workflow ingests order, shipment, and invoice data, reconciles exceptions against contracted rates and SLAs, routes anomalies to the right operations owner, and updates the data warehouse and BI dashboards. Steps are predefined, every transition is logged, and humans approve the high-impact actions.
Manufacturing: document intelligence at the line
A representative workflow where lab reports, quality forms, and supplier certificates flow through a vision-and-reasoning agent that extracts measurements, validates them against tolerances, and writes structured records into the manufacturing data warehouse for downstream BI and SPC analysis.
When Do You Need AI Agent Development Services?
- Your analysts spend hours answering the same recurring questions in Power BI, Tableau, or Snowflake.
- You have a content engine that depends on people manually reading GA4 and Search Console every week.
- Operations teams reconcile orders, invoices, and shipments by hand across ERP, WMS, and TMS.
- You ran AI experiments on laptops and now need a production-grade, governed setup.
- You want to use Vertex AI, LangGraph, or Anthropic Claude in your business but lack a clear architecture.
- You have tried generic AI chatbots and they hallucinate against your data instead of using it.
Why Choose Witanalytica for AI Agent Development?
We Are a Data Company First
Every Witanalytica AI agent is grounded in real, governed data: BigQuery, Snowflake, Power BI, GA4, Search Console, ERPs. We come from 18+ years of BI and data engineering, so we do not let agents hallucinate against your numbers.
Architecture Fit, Not Vendor Loyalty
We are not a Microsoft, Google, or Anthropic reseller. We pick LangGraph, Vertex AI, OpenClaw, Claude, or OpenAI based on the workflow, not the partner badge. Predictable processes get LangGraph. Broad autonomy gets a sandboxed agent. Conversational analytics gets a grounded copilot.
Human-in-the-Loop by Default
Our agents do not silently push to production. They draft, propose, and queue actions for human approval where the cost of being wrong is non-trivial. You stay in control while still getting the speed.
Built for Audit and Cost Control
Every run is logged with inputs, reasoning traces, tool calls, model costs, and outcomes. We design for cost predictability from day one: smaller models where possible, cached tool calls, and clear monitoring dashboards.
Production, Not Demoware
We have implemented agentic AI in pilots and production setups for analytics, content operations, and process automation, and we know the difference between a slick demo and an agent that runs reliably for months.
Aligned with Your Industries
Logistics, supply chain, retail, e-commerce, manufacturing, healthcare, and affiliate marketing. The same industries where we already build BI and data engineering practices.
Our AI Agent Delivery Process
We map the workflow you want to automate end to end. Inputs, decision points, exceptions, current owners, and what good looks like. We separate the parts that should be deterministic process automation from the parts where an autonomous agent adds real value.
We choose the right pattern: a stateful LangGraph workflow when steps are predictable and audit matters, an autonomous agent in a sandboxed environment when the work needs broad system access. We design the guardrails, the data access policies, and the human-in-the-loop checkpoints up front.
We connect the agent to your real data and systems: GA4, GSC, BigQuery, Snowflake, Power BI, ERP, CRM, ticketing, CMS, messaging. Read access first, with read-write surfaces introduced behind explicit approvals.
We run the agent on real data in a sandboxed environment, with all actions surfaced for human review before they reach production systems. We tune prompts, tools, and decision policies until quality and reliability meet your bar.
We deploy on Google Cloud, your existing infrastructure, or a dedicated VM. Every run is logged with inputs, reasoning traces, tool calls, costs, and outcomes. Dashboards make agent behavior as observable as any other production system.
We monitor success rates, latency, model spend, and human override frequency. We iterate on prompts, tools, and which model handles which step, swapping smaller models in where they perform well to keep costs predictable.
We map the workflow you want to automate end to end. Inputs, decision points, exceptions, current owners, and what good looks like. We separate the parts that should be deterministic process automation from the parts where an autonomous agent adds real value.
TESTIMONIALS
Witanalytica has been an awesome team to work with. They have such a talented team with a broad range of expertise in software development, BI and data analysis - which have all been instrumental in helping us achieve our technical goals. We truly value their partnership and look forward to continuing to work together.
Gregg Bansavage
CIO, RBW Logistics
Witanalytica has been an excellent partner in managing and optimizing our Tableau environment. Their team’s technical expertise and proactive support have streamlined our reporting processes, improved dashboard performance, and provided valuable insights to our business. Their responsiveness and deep understanding of data analytics make them a trusted extension of our own team.
Mark Lack
Director of Data Analytics and AI, The Ubique Group
Witanalytica helped us transition from Excel to a dynamic dashboard, allowing us to view all the relevant data and the KPIs that we track as a business. Instead of having our developers code an interface for weeks, we can now instantly accomplish this process through an interface, eliminating the need for manual coding.
Radu Albastroiu
Startup Founder, masinilacheie.ro
Witanalytica’s expertise in big data engineering and visualization complements our digital media audit and customer analytics services. Collaborating with them allows us to deliver end-to-end analytics solutions and services, without the risks and investments associated with building these capabilities in-house.
Silviu Toma
Senior Partner, Microanalytics
Working with Witanalytica has transformed our approach to reporting. Their expertise in PowerBI enabled us to go beyond the limited capabilities of Excel, allowing us to provide our clients with dynamic and visually captivating PowerBI dashboards. This capability has facilitated rapid testing, iteration, and the collection of customer feedback to improve our platform.
Alin Rosca
Startup Founder, RepsMate
Working with Witanalytica has been a consistently positive experience. They are responsive, professional, and approach every revision with patience and precision. What sets them apart is a strong understanding of supply chain management, inventory planning, and sales operations, which makes collaboration efficient and ensures deliverables align with real business needs. They have also worked effectively across multiple departments in our organization and manage a 6-7 hour time zone difference seamlessly. I would confidently recommend them to any organization seeking a skilled and dependable analytics partner.
Rubin Chen
Supply Chain VP, The Ubique Group
Frameworks, Models, and Platforms We Use
LangGraph
LangGraph is the right framework when steps are predefined, state matters, and every transition needs to be auditable. We use it for process automation agents that run reliably in production.
OpenClaw
OpenClaw is positioned as a personal AI assistant. We deploy it differently: in isolated VMs inside the customer infrastructure, with sandboxing, audit logging, and access restricted to specific tools. Useful when an agent needs broad autonomy in a controlled environment.
Anthropic Claude
Our go-to model for complex reasoning, long-context analysis, and tasks where instruction-following accuracy matters most. We use Claude across analytics agents, document intelligence, and agentic planning steps.
OpenAI
GPT-4o for general reasoning and coding tasks, GPT Image 2 as our current default for generating high-quality hero imagery in content operations agents. We mix OpenAI and other providers per task based on quality, latency, and cost.
Data Sources and BI
Agents are only as good as the data they read. We connect them to BigQuery, Snowflake, GA4, Search Console, ERPs, WHMs, CRMs and other operational systems with proper auth and row-level security.
Cloud and Deployment
We deploy production agents on Google Cloud (Compute Engine, Cloud Run), AWS, or Azure. Where data residency or compliance requires it, we run agents in dedicated isolated VMs inside your own infrastructure.
Our AI Agent Development Pricing Models
Transparent pricing built for long-term partnerships, not one-off transactions.
On-Demand Expertise
All tasks are tracked, and the corresponding invoice of the delivered services is billed monthly.
| Activity | Hourly Rate |
|---|---|
| AI Agents Development and Implementation | $100 |
| Data Engineering & Database Administration | $110 |
| Business Intelligence Reporting | $90 |
| Data Science | $120 |
Reserved Capacity Agreement
- Pre-purchase a package of monthly working hours that guarantees reserved capacity and priority availability, regardless of our workload.
- Because this capacity is exclusively allocated to you, unused hours do not carry over to the following month.
| Hours Package | Price |
|---|---|
| Every 50 hours | $4,500 10% savings |
Alternatively, we also offer project-based pricing
For well-defined engagements, we scope the full project upfront and agree on a fixed fee, so you know exactly what to expect.
Insights on Agentic AI
How we think about AI agents, frameworks, and production deployments.
5 articles
LangGraph vs OpenClaw: Predictable Process Automation vs Autonomous Agents
LangGraph and OpenClaw solve different problems. LangGraph is the right choice when steps are predefined and audit matters. OpenClaw earns its keep when autonomy is the point. Here is how we choose between them in production.
OpenClaw for Companies: Why a Powerful AI Agent Does Not Belong on Your Laptop
OpenClaw is powerful precisely because it can read everything and change everything. That is exactly why it should not run on your laptop, your Mac Studio, or any device that holds your real files. Here is how we deploy it safely in disposable cloud VMs.
LLMs for Business Leaders: Applications Across Departments
LLMs go beyond chatbots. Learn how business leaders apply them to customer service, marketing automation, HR workflows, and internal knowledge management.
LLMs for Data Analytics: Extracting Business Insights with AI
LLMs do more than generate text. See how they integrate with analytics workflows to surface patterns, automate reporting, and speed up decision-making.
Generative AI Hyper-Personalization: Beyond Segmentation
Generative AI enables one-to-one personalization at scale. Explore how it challenges segment-based marketing and transforms targeting and customer experience.
Your Goals, Our Expertise
We start from your strategic objectives and work our way back to the right mix of solutions and technologies, not the other way round.
Book a Consulting CallAI Agent Development FAQs
A chatbot answers a single question with a single response. An AI agent reasons over multiple steps, calls tools, reads and writes data, and works toward an outcome that may take seconds or hours. An agent has memory, state, and the ability to act on systems, not just talk.
Agentic AI is the design pattern where AI systems take autonomous, multi-step actions toward goals, using tools, data sources, and other agents. It is the evolution from prompt-driven assistants to production workflow agents. Google now describes its enterprise stack as the Gemini Enterprise Agent Platform, the evolution of Vertex AI Agent Builder.
LangGraph fits process automation: when steps are predefined, every transition matters, and audit and reliability are critical. OpenClaw and similar autonomous agents fit broader, more open-ended work where the agent needs to figure out the steps itself, ideally inside a sandboxed environment. We mix the two when the workflow has both a deterministic backbone and exploratory pockets.
Agents run inside your cloud or in an environment you control. Data access is scoped with service accounts, row-level security, and read-only by default. We log every tool call. For sensitive deployments we use isolated VMs, network policies, and human approval before any write. Our processes align with GDPR.
Yes. The agents we build are grounded on your existing semantic layer: Power BI datasets, Tableau data sources, dbt models, BigQuery views, or Snowflake schemas. The agent consumes the same governed metrics your team already trusts.
In our experience, no. They remove repetitive work and accelerate first drafts so your team focuses on judgement, customer-facing decisions, and edge cases. Our default design keeps a human in the loop for any action that has real business impact.
A focused agent for one workflow, grounded on existing data, typically goes from discovery to a supervised production pilot in 4 to 8 weeks. Broader agentic systems with multiple integrations and approval workflows take longer.
We are model-agnostic. We use Google Gemini models (typically called directly through the Gemini API), Anthropic Claude, and OpenAI models including GPT Image 2 for image generation. For clients standardized on Google Cloud, we also design deployments on Vertex AI and the Gemini Enterprise Agent Platform. We pick per task, often mixing a high-capability model for reasoning with a smaller, cheaper model for routine steps.
We set per-run and per-day budgets, cache tool calls and embeddings, and route routine steps to smaller models. We monitor token usage in dashboards alongside business outcomes so cost is always visible against value.
Yes. We often work alongside in-house teams: we design the architecture, the guardrails, and the evaluation framework, then either implement end to end or hand it off with documentation and training.
We offer two engagement models with transparent pricing.
On-Demand Expertise
All work is tracked and billed monthly at hourly rates:
- AI Agents Development and Implementation - $100/hr
- Data Engineering & Database Administration - $110/hr
- Business Intelligence Reporting - $90/hr
- Data Science - $120/hr
Reserved Capacity Agreement
- Pre-purchase a 50-hour monthly package at $4,500 (10% savings)
- Guaranteed priority availability regardless of our workload
We also offer project-based pricing for well-defined engagements.
Contact us to discuss the best fit for your needs.