What Simform’s Claude Select Partner Status Means for AI-Native Product Engineering

Simform has been named a Select partner in the Services Track of Anthropic’s Claude Partner Network. The designation reflects the Claude practice our engineering teams have built across customer delivery and our own software development, with Claude Code in use from requirement analysis through release.

The recognition comes as Claude Code moves from individual experimentation into team-wide delivery. A single developer can get value from Claude Code within an afternoon. Getting the same value across squads, repositories, and release cycles is harder. Context resets between sessions, engineering standards vary between developers, generated code adds pressure to review queues, and token spend becomes harder to predict.

In our experience, the delivery system and architecture choices determine how much of Claude’s speed actually reaches production.

That gap is sharpest for mid-sized software product companies, where a lean engineering team carries a growing roadmap, a legacy codebase, and customer commitments at the same time.

Select status strengthens the Anthropic alignment, technical support, and Claude engineering depth Simform brings to those teams, and to enterprises building AI into their own software.

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 Simform, that evidence starts with 150+ certified engineers who use Claude in day-to-day delivery.

For engineering leaders evaluating a Claude partner, those criteria provide a concrete starting point. The next discussion can focus on whether a partner’s experience fits the codebase, team structure, roadmap, and type of Claude work being planned.

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:

• Deliver more of the roadmap with the same team, by moving Claude Code from individual use into a coordinated delivery workflow.
• Protect code quality as output grows, with human review and approval at scope, design, code, and release.
• Apply one set of engineering standards across squads, with conventions, testing practices, and architecture expectations encoded into the workflow.
• Keep token spend predictable, with budget, time, and turn limits on every agent task.
• Adopt new Claude capabilities systematically, alongside Simform’s Claude engineering experience.
• Shorten the path from working prototypes to production, with validation, governance, and integration designed into the first build.

For teams running on Azure, Simform’s Azure Expert MSP status and all three Microsoft Solutions Partner designations let us handle the platform, identity, and security work within the same program.

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

We use PexAI, our proprietary Agentic SDLC platform, to coordinate Claude and other foundation models across discovery, design, implementation, testing, deployment, and operations. It packages Simform’s software engineering standards into reusable agents, skills, and workflows that work from shared project context.

Keep project context consistent across agents

PexAI maintains context about the application, its architecture, repositories, engineering conventions, and delivery requirements. That context remains available across tasks, including changes that span frontend, backend, data, or infrastructure.

This reduces the need to reconstruct the application context each time work moves to another agent or stage of delivery.

Carry a requirement through delivery

PexAI coordinates 19 specialist agents and 45 reusable skills across the SDLC. A requirement can move from clarification and planning through implementation, review, correction, documentation, and release without rebuilding its context at each stage.

The workflow keeps the original requirement connected to the implementation and the checks that follow it.

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 stay consistent across teams while adapting to each customer’s engineering environment.

This gives engineering teams a common delivery model without forcing every project into identical technology or architecture choices.

Set limits on agent execution

Every PexAI task runs in an isolated environment with budget, time, and turn limits, keeping token consumption tied to the task being performed. Four human approval gates cover scope, design, code, and release, so decisions carrying engineering or release risk remain with people.

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.

Software product companies and enterprises are also putting Claude inside the software their customers and employees use, from document processing to in-app assistants.

These use cases introduce runtime decisions around the information Claude can access, the actions its outputs can trigger, and the visibility teams retain after deployment.

Control what context Claude can access

Each feature needs a defined set of information sources, such as product data, customer data, internal knowledge, or previous actions. Teams need to decide which sources Claude can use, how current those sources need to be, and what data remains outside the model’s reach.

Those boundaries become part of the application architecture rather than a prompt-level decision.

Validate outputs before they trigger 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, which can include schema validation, business rules, model evaluations, or human approval before the output moves downstream.

Tool permissions follow the same principle. An agent that reads account information should not hold the same permissions as one that changes an order or modifies production code.

Trace context, tool calls, retries, and cost

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, an integration failure, or a workflow design problem.

It also gives teams an operational record of how the system behaves once usage grows beyond the original implementation team.

The first engagement below used Claude Code across software delivery. The second used Claude Sonnet inside a customer-facing document workflow. Both show how Claude changes the engineering work around a system, not only the model interaction itself.

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, and 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. Our engineers then executed the work, reviewed each side, and completed end-to-end testing.

The same workflow extended to testing and regression. Regression cycles that took roughly 30 to 34 hours of manual testing now run in about 3 to 4, so fixes reach customers as soon as they clear review. Work initially estimated at roughly 400 engineering hours was delivered in about 100, a fourfold gain in delivery capacity on the same scope.

Those gains came from where Claude sat in the process. Requirements were checked before implementation, frontend and backend changes were planned together, and engineers retained ownership of every product and engineering decision.

Student transcript processing with 99% less manual entry across changing formats

An education technology company needed its platform to turn student transcripts into structured course and credit data. Its existing pipeline combined a custom-trained document extraction 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, so the model interprets text and layout together and document structure no longer has to be reconstructed 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.

Claude releases will continue to change what teams can automate in software delivery and what they can build into products. Engineering teams need a way to absorb those capabilities without redesigning their delivery model for every new project.

A delivery workflow proven on one codebase becomes the starting point for the next squad, and a Claude-powered feature that holds up with customers becomes the reference for the next capability on the roadmap.

Our closer alignment with Anthropic gives us a direct route to evaluate new Claude capabilities against those existing engineering patterns as they arrive.

For teams deciding where to begin, a contained first step works well. That might be one feature stream, one legacy module, or one product capability with a clear owner and a measurable starting point. Ready to scale Claude across your engineering team or your product? Connect with our engineers.