Custom Development Services—with AI at the application layer
We deliver AI software development services that show up where users work: in-app copilots, document AI (IDP), knowledge assistants (RAG), recommendations, anomaly alerts, and voice—plus AI consulting and integration for your existing systems.
AI development services explained
We treat AI as a general capability—valuable only when it improves a workflow inside your product. HyperSense provides AI software development, AI consulting services, and AI integration services alongside custom web and mobile engineering.
We’re ISO/IEC 27001 & 9001 certified, model-agnostic, and outcome-focused, so you get application-level AI that’s secure, governed, and measured.
Building AI at application level
Custom software development
Craft software that aligns with your goals—with AI woven into key user flows.- In-app copilots and guided actions (application-level AI development)
- Automations that reduce repetitive work and errors
- AI-ready architecture with KPI baselines and observability
AI consulting & integration
Bring AI integration services to your current stack—without disruption.- Document AI (IDP): classify → extract → validate → post
- Knowledge assistants (RAG) with citations over your content
- AI chatbot development, recommendations, anomaly/risk signals, and voice
Hire a software development team
Spin up a dedicated team in ~4 weeks that ships AI-infused features.Find out more- Full-time engineers across web/mobile/backend
- AI software developers and AI consultants embedded in your workflows
- ISO-aligned process, transparent estimates, shared KPIs
UI/UX design
Design AI-assisted journeys users trust.- Clear states and explanations/citations for AI answers
- Assistive UX that speeds task completion and adoption
- Research-driven flows for new AI features
Mobile application development services
Create engaging mobile apps with built-in AI.- In-app copilots, contextual prompts, smart capture (IDP)
- Voice & real-time assistants for on-the-go tasks
- Analytics on AI feature adoption and impact
Web development services
Build scalable web platforms with AI where it helps most.- Knowledge assistants (RAG) in your UI (policies, docs, product data)
- IDP portals for claims/POs/KYC with human-in-the-loop
- Personalized experiences to lift engagement and retention
Digital business transformation consulting
Turn strategy into AI-enabled operations.- Process mapping → agentic automation opportunities
- AI strategy consulting, AI readiness & ROI assessments
- 90-day plans and adoption playbooks
IT consultancy services
Align technology, data governance, and security with business goals.- ISO-aligned controls, least-privilege access, auditability
- MLOps consulting with evaluation & cost guardrails
- Platform choices that avoid vendor lock-in
Product discovery services - TechBoost Program
Validate a high-value use case and ship an AI feature fast.- Prioritized backlog and KPI model
- Prototype & usability testing for in-app AI
- Delivery plan for a 4-week AI Feature Sprint
R&D software engineering services
Explore, test, and de-risk AI before scaling.- Rapid proofs for RAG, IDP, voice, recommendations
- Generative AI development and machine learning consulting as needed
- Hand-off to production with governance in place
AI at Work: where we add value fastest
We start from your business process and KPI—then deliver application-level AI that combines knowledge-base assistants(RAG) and agentic workflows to remove friction and ship value fast. Our AI consulting & integration approach focuses on outcomes you can measure:
Knowledge assistant for your teams (RAG)
Ask in plain language; get cited answers from policies, wiki pages, tickets, and docs—cut search time and rework.Decision & approval automation (agentic workflows)
Goal-driven agents gather data, check rules, and propose decisions with human approval—faster onboarding, reviews, and compliance.Customer support copilot
Personalized 24/7 chat/voice with safe system actions—higher first-contact resolution and better CSAT.Document & case intake (IDP)
Classify, extract, validate, and post data from invoices, claims, contracts—less manual entry, audit-ready.Pricing, recommendations & promotions
Dynamic pricing and next-best actions that lift conversion and retention—explainable and controllable.Ops forecasting & anomaly alerts
Detect fraud, quality issues, and delays; forecast demand and ETAs to trigger proactive tasks.
Our Process: Simplifying Your Tech Journey—with AI at the application layer
1.
Discovery & Analysis
Align on business objectives, processes, and KPIs—not just features. Identify quick wins for AI software development (e.g., knowledge assistants/RAG, document AI, in-app copilots, agentic workflows).
Run a light AI readiness & ROI check: data sources, systems, constraints, and success metrics.
2.
Strategic Planning
Define the in-app experience (where the AI shows up in your UI and how it helps). Choose the right path (RAG, IDP, recommendations, agentic automation) and a model-agnostic approach.
Outline security & governance (privacy, access, audit), plus a high-level plan for AI integration services with your current stack.
3.
Design & Development
Design assistive UX (clear states, explanations/citations, approvals) so AI adds value without adding friction.
Build the feature inside your app (e.g., knowledge assistant with citations, document & case intake, customer support copilot, decision/approval automation). Integrate safely with your systems and data, keeping vendor choices flexible.
4.
Quality Assurance & Testing
Go beyond functional tests—add AI evaluations for quality, faithfulness, safety, and latency. Include human-in-the-loop steps where accuracy and compliance matter.
Validate that the feature moves the KPI we aligned on in Discovery.
5.
Deployment & Support
Launch with observability and cost guardrails (usage dashboards, budgets, alerting). Provide training, run-books, and change-management so teams adopt the feature quickly.
Offer ongoing support for both the application and the AI component (prompts, retrieval, policies).
6.
Continuous Improvement
Measure impact (baseline vs. post-launch), run A/B tests, and capture user feedback. Tune prompts/retrieval, improve data coverage, and optimize costs (e.g., caching, routing tiers).
Keep things future-proof with a model-agnostic stance—switch models or approaches as your needs evolve.
Need speed? Try the AI Feature Sprint (≈4 weeks)
Pick one high-leverage use case; we design, build, harden, and ship a production feature with evaluation, governance, and KPI tracking—so you see value fast.
Case studies
Finance & fintech
Elevating user engagement & acquisition through mobile app development for a Dutch FinTech
Dutch FinTech company Tinka, aimed to to ramp up user engagement and acquisition. Collaborating closely, we crafted a user-focused mobile app, characterized by its intuitive interface and diverse payment functionalities.
Chatbot
Merging financial data with ChatGPT - HyperSense's development of ChatQBO
ChatQBO aims to transform user understanding of financial data by integrating QuickBooks Online with OpenAI's Chat GPT, facilitating accessible insights through conversational AI.Collaborating with HyperSense, the aim is to weave NLP (Natural Language Processing) technologies into this platform, enabling it to translate complex financial data into user-friendly chat dialogues.
As we navigate through the initial development phases, our technical focus ensures seamless, intuitive interactions between users and their financial data, aiming for a simplified yet comprehensive user experience in the financial data conversation.
EdTech
Transitioning to mobile - HyperSense's role in advancing a K-8 STEM school platform
A leading K-8 STEM school, with its unique blend of Project-Based Learning, recognized the need for a comprehensive digital overhaul. In collaboration with HyperSense, the ambition expanded from merely transitioning to mobile.
Alongside the mobile evolution using Flutter, which is currently in Beta-testing and showing promising outcomes, we are also immersed in cloud migration efforts and the development of a custom-built web platform.
This full-stack project, still in its progressive stages, promises to transform the school's digital infrastructure comprehensively. As our partnership deepens, we are committed to delivering a state-of-the-art experience for students, teachers, and parents.
Our tech stack
Built for Application-Level AI
Our technological skills span modern web, mobile, and cloud—designed to bring AI into the application layer. Whether you need AI software development, AI consulting & integration services, or generative AI development, we build features your users feel in-product and govern them for security, quality, and cost.
Frontend development:
Languages
HTML5, CSS3, JavaScript/TypeScript
Frameworks/Libraries
React, Angular, Vue.js
AI in the UI
Assistive UX (copilot panels, explainability/citations, approvals), accessibility, analytics for AI feature adoption
Backend & APIs:
Languages
Node.js, Python, Java, .NET, PHP
Frameworks
Express/Fastify, FastAPI, Spring Boot, ASP.NET Core, Laravel
Data & events
REST/GraphQL, Webhooks, event-driven patterns for agent actions and automations
Mobile development:
Native
Swift (iOS), Kotlin (Android)
Cross-platforms
Flutter, React Native
AI on mobile
In-app copilots, smart capture (IDP), voice, on-device friendly patterns (caching/offline)
Cloud platforms:
Primary
AWS (Partner), plus Azure, Google Cloud Platform
Common services
Serverless (Lambda/Functions), containers (ECS/Fargate/Kubernetes), object stores, managed DBs, CDN, secrets/keys
AI & Retrieval Layer (our “application-level AI” core):
Model access (LLM gateway)
Amazon Bedrock, OpenAI/Azure OpenAI, Gemini—model-agnostic routing and prompt caching
RAG/Knowledge
Embeddings, vector search (pgvector/Postgres, OpenSearch k-NN, Pinecone, Weaviate, Qdrant), citations & provenance
IDP (Document AI)
Classification → extraction → validation → posting, with human-in-the-loop
Agentic workflows
Tool/function calling into your systems, approval lanes, action logs
Interop
(Optional) MCP-ready integrations to expose safe tools/data to agents
Data engineering & analytics:
Pipelines/lakes
AWS Glue/Athena, EMR, Step Functions, Airflow, dbt; S3/Parquet; Lakehouse (Delta/Iceberg)
Streams
Amazon Kinesis, Kafka
Warehouses
Redshift, BigQuery, Snowflake
BI/observability
Dashboards for KPI impact, usage, and cost of AI features
MLOps, evaluation & governance:
MLOps consulting
Monitoring, regression tests, drift checks
Eval & safety
Quality/ faithfulness/ safety/ latency scorecards; red-teaming; guardrails
Cost control
Token budgets, prompt caching, tiered model routing, usage alerts
Telemetry
CloudWatch, OpenTelemetry, Datadog—feature-level metrics
DevOps & CI/CD:
Tools
Docker, Kubernetes, Terraform, GitHub Actions, GitLab CI/CD, Jenkins
Practices
Blue/green and canary deploys, feature flags, automated rollbacks for AI-powered features
Security & Compliance (privacy by design):
Standards
ISO 9001:2015, ISO/IEC 27001:2018; GDPR/CCPA alignment
Access
SSO/SAML, OAuth 2.0/OIDC, RBAC, agent identities & least-privilege policies
Data
Encryption in transit/at rest, customer-managed keys, PII/PHI handling, audit logs, data residency options
Controls
Model/data-usage policies (your data is never used to train third-party models without consent)
IoT and integration:
Protocols
MQTT, CoAP, HTTP
Platforms
AWS IoT Core, Azure IoT Hub
Use in AI
Device telemetry → anomaly detection, predictive maintenance, automated workflows
Multimedia & Real-Time Communication:
Tech
WebRTC, VoIP/SIP, streaming (HLS, MPEG-DASH)
Use in AI
Real-time voice assistants, call summarization, coaching, and automated follow-ups
Design and prototyping:
Tools
Figma, Adobe XD, Sketch
Practices
Wireframing, assistive UX patterns (explanations, citations, confidence states), user testing for AI features
Why this matters
This stack lets us deliver AI software development services that are model-agnostic, secure, and measurable—so whether it’s a knowledge assistant (RAG), AI chatbot development, intelligent document processing, or agentic automation, we can integrate it directly into your product and prove the impact.
Insights into software development
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Balancing AI Integration Costs: Build From Scratch or Upgrade Existing Systems?
Discover detailed TCO insights for integrating AI: compare greenfield development with embedding AI into existing infrastructure. Learn cost drivers, ROI, and when each approach makes financial sense.
Cloud vs. On‑Premise Infrastructure: Comparison Guide for IT Leaders
Cloud adoption soars, yet on-premise remains vital for control, cost, and compliance. This guide weighs CapEx vs OpEx, latency, security, DR, and hybrid models to help IT leaders choose wisely.
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Contact us and get your project moving
Describe your needs to us and we'll work together to craft the perfect software solution. Tell us your timeframes, your targets or KPIs, and any critical challenges or difficulties. You're at the right place — our team is eager to study and prioritize next steps together with you.Have a project in mind?