What Simform’s Claude Select Partnership Status Means for Enterprise AI Engineering?

Simform has been named a Select partner in the Services Track of Anthropic’s Claude Partner Network. The designation reflects the Claude practice we have built across customer delivery and our own engineering operations.

The recognition comes as enterprise Claude adoption moves past experimentation. A pilot can run on a narrow dataset with an engineer watching every output. A production system has to handle unfamiliar inputs, connect to systems of record, pass security review, and hold a predictable cost as usage grows. In our experience, these engineering demands shape Claude outcomes as much as the model itself.

Select status strengthens the Anthropic alignment, technical support, and Claude engineering depth Simform brings to that work.

We apply Claude in two connected areas: building AI systems for business workflows and automating the software delivery process behind them.

The Claude Partner Network is how Anthropic works with the firms that put Claude into production for customers. The Services Track recognizes partners for building a certified delivery team, delivering Claude use cases for clients, and proving the work through public customer references. Partners advance through Certified, Select, Preferred, and Global Premier tiers.

Since the Claude Partner Network launched in March 2026, Anthropic has received more than 40,000 applications. Simform qualified for Select status based on its Claude-certified practitioners, production deployments, and public customer evidence.

For enterprise teams evaluating a Claude engineering partner, that distinction matters because the status reflects demonstrated practice across the people doing the work, Claude systems running in production, and customer outcomes documented publicly.

Select status gives Simform’s teams closer access to Anthropic through technical support and partner resources. For our clients, that creates a stronger basis to:

• Shorten the path from prototype to production, with validation, governance, and integration designed into the first build.
• Resolve model behavior questions faster, with access to Anthropic technical support and partner resources alongside Simform’s Claude engineering experience.
• Adopt new Claude capabilities systematically, by evaluating where they fit into enterprise agentic systems and repeatable engineering workflows.
• Keep engineering control, with human approval at scope, design, code, and release.

For organizations already operating on Azure, we can also address the surrounding platform, identity, networking, security, and operations requirements as part of the same program.

Simform holds Azure Expert MSP status and all three Microsoft Solutions Partner designations, bringing that Microsoft engineering depth alongside our Claude practice

Our approach to engineering Claude for enterprise use is to build the controls that make Claude dependable before usage scales. Five engineering decisions carry the most weight.

1. Control what enterprise context Claude can access

Each workflow needs a defined set of information sources. That may include application data, internal knowledge, customer records, policies, or previous actions.

Teams need to decide which sources Claude can use, how current those sources need to be, and what data stays outside the model’s reach.

2. Validate Claude outputs before they trigger downstream actions

A response shown to a user carries different risk from one that updates a system of record or starts another workflow.

Higher-impact actions need stronger checks. Depending on the use case, that can include schema validation, business rules, model evaluations, or human approval before the output moves downstream.

3. Match tool permissions to the action

An agent that reads account information should not have the same permissions as one that changes an order, approves an expense, or modifies production code.

Permissions, escalation paths, and execution controls need to reflect what the agent is allowed to do.

4. Integrate Claude with existing identity, APIs, and data contracts

Claude still has to work with the technology already running the business. That includes APIs, identity controls, data contracts, logging, and operational processes.

In many enterprise systems, connecting model behavior to this environment requires more engineering than the model call itself.

5. Trace context, tool calls, retries, and human intervention

Teams need visibility into which context Claude used, which tools it called, where retries occurred, when a person intervened, and what each completed task cost.

That trace helps engineering teams distinguish a model issue from a data problem, integration failure, or workflow design problem.

At Simform, Claude is becoming part of a coordinated engineering system that spans discovery through deployment and operations.

PexAI, our proprietary Agentic SDLC platform, provides the delivery layer around Claude and other foundation models. It brings Simform’s software engineering standards into reusable agents, skills, and workflows that operate on shared project context rather than isolated developer prompts.

Keep project context consistent across agents

PexAI maintains shared context about the application, its architecture, repositories, engineering conventions, and delivery requirements.

That context stays available across tasks, including changes that span frontend, backend, data, or infrastructure.

Carry a requirement through delivery

PexAI coordinates 19 specialist agents and 45 reusable skills across discovery, design, implementation, testing, deployment, and operations.

A requirement can move from clarification and planning through implementation, review, correction, documentation, and release without rebuilding its context at each stage.

Encode engineering standards into the workflow

We codify proven engineering and delivery practices in PexAI as reusable skills and workflow steps.

Architecture expectations, coding standards, testing practices, and delivery controls can then be applied consistently across teams while still being adapted to a customer’s engineering environment.

Keep agent execution inside defined limits

Every PexAI task runs in an isolated environment with budget, time, and turn limits.

Four human approval gates cover scope, design, code, and release. This keeps people involved at the points where engineering judgment or production risk requires a decision.

Across applicable engagements, Simform targets up to 40% faster modernization, 40% faster development, and 30% lower token costs, measured against each client’s existing delivery baseline.

Our Claude practice spans both the systems we build for customers and the engineering workflows used to deliver them. These two examples show how that looks in production.

Student transcript processing: 99% less manual entry across changing document formats

An education technology company needed to turn student transcripts into structured course and credit data. Its existing pipeline combined a custom-trained Azure Document Intelligence model, an LLM refinement step, and rule-based normalization.

The pipeline worked on formats it had been trained to recognize. A new school district or transcript layout meant collecting samples, annotating them, training another model, validating it, and deploying the change before the system could process that format reliably.

We replaced that pipeline with a Claude Sonnet 4.6 extraction service. The service passes the raw PDF to Claude as a native document block, allowing the model to interpret text and layout together instead of reconstructing document structure after OCR.

The domain rules moved into the extraction workflow as well. The system prompt defines the output schema, school-year and term rules, credit mappings, and how missing values should be handled. Changes to those rules can be reviewed and deployed without starting another model-training cycle.

The production system reduced manual transcript-entry effort by 99% and handles previously unseen formats at more than 95% accuracy.

Youth sports camp platform using Claude across requirements, implementation, and testing

For a US-based sports technology company, we used Claude Code as part of the engineering workflow for a multi-tenant sports camp platform built on React, Node.js, Azure, and Stripe.

Claude entered the process before implementation. We built a custom command that compared each user story with its Figma screens and surfaced inconsistencies or missing details. Our engineers worked through those findings with Claude, while unresolved product decisions went back to the company before development started.

For implementation, Claude worked across the frontend and backend repositories in one continuous feature session. It produced a combined plan covering affected files, APIs, data flow, and dependencies before our engineers executed the work, reviewed each side, and completed end-to-end testing.

The same workflow was applied to testing and regression work. Regression cycles that previously took roughly 30–34 hours were reduced to around 3–4 hours, while work initially estimated at roughly 400 engineering hours was delivered in about 100.

The important part was not simply giving developers access to Claude Code. Claude was placed inside a delivery process where requirements were checked before implementation, frontend and backend changes were planned together, and engineers retained ownership of every product and engineering decision.

Claude becomes more valuable when enterprises can apply it consistently across products, workflows, and software delivery without rebuilding the operating model each time.

The Anthropic partnership brings our teams closer to the model provider, while PexAI carries proven Claude engineering patterns into repeatable customer delivery.

For teams moving a Claude initiative out of pilot, the place to start is the workflow you want in production and the surrounding controls it will need. Ready to take a Claude workflow into production? Connect with our team.