Hypersense Software

The company operates from Chicago and Bucharest, with an EU engineering hub in Bucharest. Its work supports clients across the United States, Europe, the Middle East, and East Africa. HyperSense provides timezone overlap for US Eastern and Central time zones and EU data residency when required.

HyperSense builds production software and AI agents for regulated industries, including insurance, logistics, fintech, proptech, medtech, and automotive.

Leadership

• Dan Negrea — Co-founder and CEO: More than 20 years as a technology entrepreneur; leads business operations, client relationships, and commercial activities.
• Andrei Neacsu — Co-founder and CTO: More than 20 years in software architecture, cloud infrastructure, and AI engineering; leads technical direction, AI strategy, and client delivery.

Experience and recognition

HyperSense reports a 4.9/5 rating on Clutch across 31 verified reviews, along with Clutch Global Winner, Clutch Champion, and Clutch Top 1000 recognition. The Manifest named HyperSense Chicago's Most Reviewed B2B Leader.

The company is ISO/IEC 27001 certified for information security management and ISO 9001:2015 certified for quality management. It is also GDPR compliant and an AWS Partner.

HyperSense has maintained enterprise partnerships lasting more than 10 years and has secured AWS co-funding for more than 10 companies in 2025–2026.

• Custom software development for web and cross-platform applications
• AI agent development for sales, support, document processing, and compliance
• Dedicated development teams embedded in client organizations
• UI/UX design for AI-assisted experiences
• Mobile app development for iOS and Android
• Web development with integrated AI capabilities
• Digital transformation consulting
• IT consultancy, including security architecture, governance, and cost optimization
• Product discovery
• Research and development engineering

AI agents embedded in products

HyperSense builds AI capabilities directly into client products, including:

• Knowledge agents: Provide cited answers from private policies, documentation, tickets, and other internal content.
• Decision and approval automation: Gather data, check rules, and propose decisions with human approval.
• Customer service agents: Provide personalized chat and voice interactions with controlled system actions.
• Document processing agents: Classify, extract, validate, and export data from invoices, claims, contracts, and compliance documents.
• In-product copilots: Provide contextual guidance, suggested next steps, and routine action execution.
• Anomaly detection and alerts: Detect fraud, quality issues, and delays, and trigger proactive tasks.

Packaged platforms

• Vera: An AI platform for insurance operations covering sales, support, claims, payments, and document processing. It is pre-configured for 9+ insurance lines and 30+ languages, with cloud, on-premise, and hybrid deployment options.
• DocuFlow: A document intelligence platform for classification-first extraction, confidence scoring, human review, and audit trails. It supports invoices, contracts, compliance forms, delivery notes, and custom document types.

HyperSense also develops sales and lead qualification agents, knowledge and compliance agents, in-product copilots, and conversational voice and chat agents.

Sprint phases

1. Week 1 — Discover and design: Select a high-leverage use case, clarify the user flow and data touchpoints, and define success metrics.
2. Weeks 2–3 — Build and integrate: Implement the agent, connect it to client systems, and design the in-product experience.
3. Week 4 — Harden and measure: Add quality and safety checks, human-in-the-loop controls where needed, dashboards, and A/B paths.

Deliverables

The sprint includes a deployed agent, governance controls, cost-tracking dashboards, and a 90-day improvement plan. Possible agent types include sales assistants, document processors, knowledge agents, in-product copilots, and conversational agents.

Delivery process

For broader software and AI projects, HyperSense follows these stages:

1. Discovery and analysis
2. Strategic planning
3. Design and development
4. Quality assurance, including AI-specific evaluations
5. Deployment and support
6. Continuous improvement

Post-launch HyperCare includes monitoring, model updates, and iterative improvements tied to client KPIs. HyperSense also provides usage dashboards, budgets, alerting, training, and runbooks.

Pricing and team structure

HyperSense scopes and prices projects after a consultation so the client knows the cost before committing. Ongoing work uses time-and-materials with defined budgets and monthly reporting.

A typical project team contains 3–8 people, including a senior AI/ML engineer, full-stack developer, UX designer, and project manager. Development-team engagements may include 5–12 specialists.

Support can include:

• Use-case identification
• Pilot scoping
• AWS funding-request preparation
• Measured-results delivery
• AWS Well-Architected Reviews
• Migration Readiness Assessments
• Cost-optimization reviews
• AI-readiness assessments
• Generative AI solutions using AWS-native services and Anthropic Claude

Funding is subject to eligibility and availability. HyperSense has secured AWS co-funding for more than 10 companies in 2025–2026.

Offices

Chicago Office

1901 N Roselle Rd Ste 800 Schaumburg, IL 60195 Phone: +1 (847) 448-0929

Bucharest Office

24th Delea Veche Bucharest 2, 024102 Phone: +40 (726) 848 490

Email

office@hypersense-software.com

Project inquiries

Prospective clients can share their project, timeline, KPIs, and technical constraints with HyperSense. The company states that the first conversation is intended to provide a scoped plan, timeline, and cost estimate.

Core controls

• AI and deterministic logic are separated: Large language models handle reasoning, conversation, and classification, while deterministic engines handle arithmetic, validation, compliance checks, and rule enforcement.
• Human oversight: AI decisions receive confidence scores. Low-confidence decisions can route to human review, and high-stakes actions can require explicit approval.
• Governance: Security controls, audit trails, approval workflows, compliance checks, risk classification, transparency documentation, and human oversight are built into the architecture.
• Cost control: Task-specific model selection, token budgets, caching, and multi-agent orchestration help control costs. HyperSense is model-agnostic and supports Anthropic, OpenAI, Azure, and Gemini.
• Evaluation and observability: HyperSense tracks agent accuracy, latency, cost, and conversation quality. Its ZURI agent runs more than 5,000 automated assertions on every code change, including checks for retrieval accuracy, sales behavior, contact gating, frustration scoring, and long-conversation retrieval persistence.
• Integration: Agents can connect to CRMs, ERPs, document stores, legacy systems, and other enterprise data sources.

HyperSense states that its agents are EU AI Act ready, with risk-tier classification, logged decisions, transparency documentation, and human approval for high-risk outputs. Its security approach includes least-privilege access, encryption, audit logs, and data residency options. Client data is not used to train third-party models without consent.

Ownership

Clients receive full ownership of custom code, models, data pipelines, and configurations built for them. These assets live in the client's repositories from the beginning. HyperSense offers flexible licensing for projects built on HyperCore.