ARTIFICIAL INTELLIGENCE AT BUILDSIMPLE
Why AI is indispensable in input management
Documents contain far more than characters on a page: images, layouts, tables, stamps, signatures, handwritten additions, and business context. Traditional OCR produces raw, machine-readable text. Buildsimple turns it into usable information.
The platform identifies the matter at hand, which information is relevant, how reliable the recognition is, and which business step must follow. This transforms unstructured customer communication into a robust basis for case processing, straight-through processing, and automated processes.
More than reading text
Buildsimple understands content, layouts, and business contexts.
More than an LLM
The right model for every task: self-trained, specialized, or generative.
More than automation
Every result remains verifiable, and your processes remain controllable.
You have the task, we have the answer
The right AI technology for every task
With Buildsimple, you have access to different AI strategies. The largest AI model is not automatically always the best or most cost-effective one.
The foundational work
Specialized machine-learning models handle the foundational work of document processing very cost-effectively in high-volume operations:
Separating, classifying, extracting
Structuring & assessing
Highly cost-effective for large volumes
When language understanding is required
Generative AI is used specifically when machine learning reaches its limits or language understanding is required.
Understands free-form language and context
Complements machine learning at its limits
With source references and guardrails
Automate cases
AI agents consolidate information from multiple documents, initiate checks, prepare follow-up questions, and propose next steps. The special feature: process decisions are not programmed laboriously, but simply described, and the agent implements them dynamically. In doing so, it operates within defined guardrails, uses only approved tools and data sources, and documents every intermediate step.
You can get started with agentic automation without having to build your own agent platform, tool integrations, and security concepts.
Describe instead of programming
Guardrails and approved tools
Every step documented
MEASURABLE, EXPLAINABLE, VERIFIABLE
Quality, control, and compliance
In regulated industries, it is not enough for AI to be "usually right."
Buildsimple makes quality measurable: with confidence scores, precision and recall metrics, plausibility rules, and manual checkpoints. For generative AI, source references, restricted response formats, and hallucination detection are added
Clear review paths, controlled approvals, and documented results ensure that business responsibility remains where it belongs: with you. This enables companies to use AI without sacrificing transparency and data protection.
Buildsimple does not automate blindly. Buildsimple makes automation controllable.
AI MUST PAY OFF IN OPERATION
Cost-effectiveness
Not every task needs the largest language model, and not every document requires a manual review. Buildsimple uses AI according to the task. Simple tasks run efficiently, while complex cases use more powerful models. You decide what proportion is processed fully automatically and what error rate is acceptable, finding your optimal balance between efficiency and reliability of results.
This reduces costs, increases throughput, and prevents AI projects from becoming permanent development and operational challenges.
AI EXPERTISE AS A PLATFORM
Why Buildsimple?
Those who build AI solutions themselves for individual use cases often experience similar patterns. Each use case results in its own solution with its own models, tools, and architectures. With every project, the number of standalone solutions grows—they diverge technologically and permanently tie up expertise.
Buildsimple instead brings together AI models, workflows, quality assurance, business logic, and operations on one platform. This creates solutions...
DIFFERENTIATING VALUE PROPOSITIONS
AI selection with a system
The right AI for every process step. Buildsimple combines different AI methods on one platform and aligns their use with your requirements. You gain a reliable foundation for document-based process automation, retain control over results, and remain technologically flexible.
Ready-made AI instead of in-house development
Models, platform features, and operations are provided by Buildsimple.
Independent
The multi-AI strategy protects against dependence on individual providers.
Controllable
Source references, scoring, and review paths make every result traceable.
Cost-effective
AI is used where it pays off—taking quality, cost and throughput into account.
FREQUENTLY ASKED QUESTIONS
FAQ
Which AI is suitable for which processing step?
Documents, spreadsheets, images and free text place different demands on processing. That is why quality, cost, speed, traceability and data protection matter when selecting an AI method. Here we explain how Buildsimple selects, combines and uses different AI models in a controlled manner.
That depends on the document type, variability, volume, desired outcome and need for control. Specialized models are often suitable for recurring standard documents, while generative AI can offer advantages for complex, infrequent or highly variable content.
Machine learning identifies and extracts defined features from documents. Generative AI can summarize or interpret more complex content, or place it in a professional context. AI agents use these results, rules and company knowledge to prepare or execute defined follow-up actions.
Buildsimple follows a multi-AI approach. This allows different models and methods to be used within the same platform, depending on which method is suitable for the respective document type and process.
Results can be safeguarded using confidence scores, source references, validation rules, master data and human review. This makes not only the result visible, but also the information on which it is based.
Depending on the selected method, models can be trained with representative sample documents or configured for a specific task using prompts. Before being used in production, results are tested with realistic test cases.
Corrections and verified results can be used to improve models, rules and processing steps. How changes are adopted, reviewed and approved should be governed through a controlled operational process.