Blog
From the Soren team
Our latest thoughts and news.
AI-native vs traditional tech consulting: what actually changes
Traditional tech consultants are slow, expensive, and often miss how your business works. Here is how an AI-native firm solves those three pain points with shoulder-to-shoulder engineering, flat transparent pricing, and delivery in days and weeks.
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Choosing an AI consulting firm: 12 questions
The single most important filter when hiring an AI consulting firm is whether they can deploy inside your own infrastructure and prove where your data lives during inference. Here are the 12 questions to ask, grouped by what they reveal, with the answer a good firm gives, the red flag to watch for, and how we'd answer each one ourselves.
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Is your company ready for AI?
A practical guide to assessing your firm's AI readiness across four pillars: technology and infrastructure, data posture, people and process, and governance and risk. Includes a self-check and how Soren's one-week assessment works.
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Custom AI development cost in 2026
A custom AI workflow typically runs from a few thousand dollars for a readiness assessment to the low-to-mid five figures for a first deployed workflow, scaling into six figures for a full private deployment. Here is what drives the number, the pricing models in the market, and why flat-rate beats the hourly meter.
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Copilot vs. custom AI: when off-the-shelf wins
Buy Microsoft Copilot or ChatGPT Enterprise when you need generic productivity for your staff. Build custom AI when the work depends on your proprietary data, has to stay inside your perimeter for compliance, or needs a workflow no off-the-shelf seat can do. Here is the honest decision rule.
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Can lawyers use ChatGPT with client documents?
Lawyers should not put privileged or confidential client material into the consumer version of ChatGPT. The ABA's duties of competence and confidentiality require knowing where the data goes and protecting it. Here is what the rules say and the compliant path for AI in legal work.
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Is ChatGPT SOC 2 / GLBA compliant?
SOC 2 describes a vendor's controls; GLBA binds the financial institution's handling of customer data no matter what tool it uses. The consumer ChatGPT app fails both for regulated financial work. Here is what each framework actually requires and what a bank must do before AI touches customer data.
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Is ChatGPT HIPAA compliant?
The consumer version of ChatGPT is not HIPAA compliant and should not be used with protected health information. Here is why, what a BAA actually covers, the compliant paths fairly compared, and the checklist any AI system has to pass before it touches PHI.
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HIPAA-compliant LLM options compared
The realistic HIPAA-compliant ways to run a large language model on protected health information are a BAA-covered managed service like Azure OpenAI or Google Vertex AI, and a private deployment inside your own infrastructure. The deciding question is whether PHI ever leaves your perimeter.
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AI audit trails: what regulators ask for
A real AI audit trail records the inputs, the output, the source documents, the model version, and the accountable human for every AI-influenced decision, all in a durable and queryable form. Here is what to log, what frameworks expect, and why it has to be designed in from day one.
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On-prem LLM deployment options compared
There are three ways to deploy a private LLM: inside your own cloud tenant, inside an isolated VPC, or fully on-premise. Each trades operational burden for control. Here is the side-by-side comparison, a decision tree, and the honest case for each.
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NIST AI RMF explained for executives
The NIST AI Risk Management Framework is a voluntary US framework that organizes AI governance around four functions: Govern, Map, Measure, and Manage. It is becoming the common language for AI risk in the United States. Here is what each function means and what you actually do about it.
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Deploying AI in regulated industries
A practical guide for healthcare, finance, and legal teams: how to adopt modern AI while keeping data, auditability, and accountability intact, mapped to HIPAA, SOC 2, GLBA, the NIST AI RMF, and the EU AI Act.
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Context is the moat
Context engineering — not the model — is the real competitive moat in enterprise AI. Here is how institutional context is encoded with retrieval-augmented generation (RAG), workflow integration, and evaluation, why RAG usually beats fine-tuning for company knowledge, and why that gap is hard for competitors to copy.
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Introducing Soren
Soren is an AI-native firm founded by MIT engineers. We build private, context-aware AI systems for banks, hospitals, law firms, and government teams, deployed inside the infrastructure they already control.