LangGraph Consulting & Development
Stateful, audit-friendly workflow agents that run reliably in production
LangGraph is our default framework for production AI workflows where steps are predefined and audit matters: process automation, conversational analytics, document intelligence, and multi-step internal copilots. This page complements our broader AI agent development services.
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What We Build with LangGraph
Stateful Workflow Design
We model your business workflow as a LangGraph state machine: nodes for each step, edges for the transitions, and a typed state object that captures everything the agent needs to remember between steps.
Tool Integration and Function Calling
We build the tool layer LangGraph nodes call: SQL tools against BigQuery or Snowflake, BI dashboards, ERP and CRM APIs, ticketing, messaging, and custom internal services. Each tool is scoped, tested, and observable.
Human-in-the-Loop Checkpoints
LangGraph supports interrupting a workflow for human review. We design the checkpoint UX: where it pauses, what the reviewer sees, how they approve or reject, and how the agent resumes.
Durable Execution and Recovery
Workflows that survive crashes, retries, and long-running steps. We use LangGraph's durable execution primitives so a multi-hour or multi-day agent run can be paused, resumed, or replayed without losing state.
Observability and Evaluation
Every node call, tool invocation, and state transition is logged. We integrate with LangSmith or your own observability stack and build evaluation harnesses so agent quality can be measured and improved over time.
Model Routing Across Providers
We use Google Gemini, Anthropic Claude, OpenAI, and smaller open-source models as the right choice per node. Reasoning-heavy nodes get a frontier model. Routine nodes get a cheaper, faster one.
When LangGraph Is the Right Choice
- You have a multi-step workflow with predefined stages where reliability and audit matter more than autonomy.
- You tried single-prompt assistants and they fail when the work needs memory, retries, or multiple tools.
- You need a human approval step in the middle of an automated workflow without losing the agent's state.
- You want to mix Gemini, Claude, and OpenAI models in the same workflow based on what each step needs.
- You need agents that survive crashes and can be replayed for compliance.
Why Choose Witanalytica for LangGraph
We Pick the Pattern, Not the Hype
LangGraph is the right framework when steps are predefined and audit matters. For broad autonomy in a controlled environment, we use OpenClaw or similar. For grounded enterprise agents on Google Cloud, we use Vertex AI / Gemini. We do not force every workflow into one tool.
Production-First Engineering
Typed state, retries, structured logs, observability, and evaluation harnesses. We engineer agents the same way we engineer data pipelines.
Grounded in Real Data
We come from years of BigQuery, Snowflake, and BI engagements. LangGraph nodes call governed data, not random embeddings.
Cost-Aware by Default
We route nodes to the cheapest model that does the job, cache tool calls, and surface costs in dashboards. Production AI does not have to be expensive.
Our LangGraph Delivery Process
We map the workflow as a graph: nodes, edges, state, decision points, exception paths, and human checkpoints. We separate the truly deterministic parts from the parts where the agent reasons.
We define and build the tools the graph calls: data queries, system writes, notifications. Read-only first, with write tools introduced behind explicit human approval.
We implement the graph with a typed state schema, retries and timeouts per node, and structured logging. The graph is testable end to end and node by node.
We run the graph on real data with all writes intercepted for review. We tune prompts, model choices, retries, and human checkpoints until quality and reliability are consistent.
We deploy LangGraph workflows to Cloud Run, your Kubernetes cluster, or LangGraph Cloud, with structured logging, alerting, and cost dashboards wired in from day one.
We track success rates, override rates, latency, and model spend. We iterate on prompts, tools, and routing to keep both quality and cost where they should be.
We map the workflow as a graph: nodes, edges, state, decision points, exception paths, and human checkpoints. We separate the truly deterministic parts from the parts where the agent reasons.
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
Our LangGraph Consulting 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.
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 CallRelated Insights
How we think about agent frameworks, predictability, and production readiness.
3 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 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.
Frequently Asked Questions
LangGraph is a low-level orchestration framework for building stateful, multi-step AI agents. It models workflows as a graph of nodes and edges, supports durable execution, human-in-the-loop checkpoints, and gives you direct control over state, retries, and observability. It is part of the LangChain ecosystem but works independently.
When the work needs memory, multiple tools, retries, branching decisions, or a human approval step. A single LLM call is fine for one-shot questions. LangGraph is the right tool when the agent has a real workflow to complete.
LangGraph fits when the steps are mostly predefined and audit matters. OpenClaw and similar autonomous agents fit when the work is open-ended and the agent needs to figure out the steps itself, ideally inside a sandboxed environment. Many production systems combine both.
No. LangGraph is model-agnostic. We routinely mix Google Gemini, Anthropic Claude, OpenAI, and smaller open-source models inside the same graph, picking per node based on the task and cost.
We integrate LangSmith or the customer's existing observability stack. Every node call, tool invocation, state transition, and model response is logged. We also build evaluation harnesses so quality is measured continuously.
Yes. LangGraph supports durable execution, so a workflow can pause for a human approval, wait for an external event, survive a crash, and resume hours or days later without losing state.
A focused workflow with a handful of nodes and tools, grounded on existing data, typically goes from discovery to a supervised production pilot in 4 to 6 weeks.
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.