Your enterprise AI stack, already built.
Build and run enterprise applications composed from pre-built blocks. Ship a production app in days, not months: audit-ready, inside your own infrastructure, with zero data egress.
Trusted by regulated enterprises to ship AI
See customer storiesBuilt to support
- SOC 2 Type II
- HIPAA
- FedRAMP
- PCI DSS
- NIST 800-53
- GDPR
- DORA
- EU AI Act
- ISO 27001
- ISO 42001
- DPDP Act 2023
- RBI Guidelines
- CERT-In
- MeitY
We help regulated enterprises put AI into production without the compliance risk, using our AI Dev Studio and Runtime to unlock speed, ownership, and audit readiness across every workflow.
Platform
All-in-one AI for enterprise
Every layer your team needs to build and run AI, in one connected platform.
Engineer the platform, not the undifferentiated heavy lifting.
Build in your IDE with CI/CD, evals and supply-chain security. Every release is versioned, tested, and carries an SBOM and build provenance, so it stays traceable.
AI capabilities, pre-built.
Compose gateways, flows, pipes, bots and agents instead of building them from scratch. Every block wires straight into private models, RAG and your tools, so it runs in production from day one.
Your whole backend, as blocks.
Configure identity, data, APIs, jobs, observability and more, instead of building from scratch. It's the 80% every enterprise app needs, so your team ships what actually differentiates you.
Ship every day, securely.
Deploy, monitor, fail over and secure, with policy and guardrails on every call. Every request writes to a tamper-evident audit trail with model and data lineage, so it stays auditable.
Any cloud. Or none.
Run on Azure, AWS, GCP or bare metal, on-prem or fully air-gapped. The platform handles provisioning, scaling and failover, so you're never locked into one vendor.
Build, your way.
Every way your team builds, on one governed platform.
Plain-language builds, governed by default.
Describe what you need in plain language. Anica composes a governed, production-ready application on the platform.
- Plain-language intent, not prompts
- Composed from maintained blocks
- Ships audit-ready to your Runtime
Composing from maintained blocks. Tenant isolation is enforced at the data layer, so nothing tenant-scoped depends on application code.
Build the whole app, end to end.
Front end, back end, data and AI assembled from one interface, then integrated to the systems you already run.
- Front end, back end, data and AI
- Bound to your data and systems
- Production UI, nothing hand-wired
Compose from pre-built services.
Define an entity once, with field validations, access rules and PII protections in the same file. The platform generates REST, GraphQL, gRPC and OData endpoints from it, enforced by IAM and audited by default.
- One definition, four API protocols
- Access rules live with the entity, not a separate service
- PII fields encrypted and kept out of the audit log
From your terminal.
Define, test and deploy by hand, or as an automated step in your build and release pipeline.
- Define, run API checks, and deploy from the command line
- Runs by hand or inside CI/CD
- Same platform, fully scripted
Own the intelligence too, not just the apps.
Run any model type privately on your own infrastructure, sharpen the ones you fine-tune on your data, and keep the whole intelligence layer as IP you own.
The AI project that failed. Now in production.
Real regulated workloads, rebuilt and shipped on kis.ai.
AI-native Mortgage Documents Processing
Pipeline that extracts and converts mortgage documents into structured, machine-readable data, the foundational layer for downstream underwriting.
- AI Insights and Context details
- Document Viewer
- Admin Panel: Document Types
- —6 months × 8 resources
- —$500K worth OpenAI tokens
- —55 mins / document (1,900 pages)
- —Failed implementation
- —PII handled ad hoc, no field-level encryption
- +4 months × 3 devs
- +3 mins / document (1,900 pages)
- +Local AI: fine-tuned Qwen 2.5
- +In production at 98% accuracy
- +PII fields encrypted, excluded from audit logs
AI-assisted Underwriter for US Mortgages
Underwriting engine that combines high-fidelity structured data with human expertise to accelerate and augment TPR decisions.
- Loan Queue for HITL processing
- Dashboard with gamified metrics
- AI Quality Control
- —3 years × 14 resources
- —Cost / document: $35
- —Accuracy: 78%
- —Access rules hardcoded per service
- +11 months × 6 resources
- +Cost / document: $8
- +Accuracy: 98%
- +Entity-level access rules (RBAC/ABAC)
AI-powered Real Estate Asset Management
Real estate asset management SaaS with AI dashboards, predictive maintenance and automated work orders, multi-tenant and IoT-integrated.
- Dashboards and AI Insights
- AI Chatbots and RAG Pipelines
- Ticketing for Human and AI agent collaboration
- —2 backend + 2 frontend + 1 AI engineer
- —12.5 person-months
- —Tenant isolation built and maintained by hand
- +2 developers (frontend + backend)
- +22 person-days
- +Multi-tenant isolation via IAM access rules
Ecommerce Concierge
AI-assisted concierge for product discovery via semantic search, with virtual try-on powered by image-processing pipelines.
- E-commerce Storefront
- Semantic Search
- Virtual Try-On
- —4 experienced developers
- —Frontend, LLMs, Diffusion, Backend, ML
- —160-240 person-days
- —API security reviewed case by case
- +2 developers (Frontend + ML)
- +20 person-days
- +OWASP-aligned APIs, Zero Trust by default
Customer Knowledge Bot
RAG application with vector and graph databases for semantic search and Graph RAG, powered by an LLM (LLaMA 3.1-8b).
- Knowledge Graph
- Customer Knowledge Bot
- Bot Configuration
- —3-4 experienced developers
- —Frontend, LLMs, Backend, Data Engineer
- —60-120 person-days
- —No default audit trail on queries
- +1 intern (frontend developer)
- +5 person-days
- +Every access request audit-logged
Migration from Pega
Warranty and Claim Management for an auto insurer, migrated from legacy Pega to kis.ai with AI-powered claim triaging and real-time policy validation.
- Warranty Management
- AI Claim Entry and Validation
- —4 Pega-certified developers + 1 solution architect
- —15 person-months
- —Audit logging bolted on later, if at all
- +2 developers (Frontend + Backend)
- +10 person-days
- +Every access request audit-logged, on-prem