Production systems, not prototypes: LLM applications, RAG, document intelligence and custom machine learning. Delivered globally and deployable on private infrastructure we supply — and we'll tell you honestly when AI is the wrong tool for the job.
Specialized engineering for AI and software delivery. Explore each practice for capabilities, stack and representative results.
Production LLM apps, retrieval-augmented generation and AI agents grounded in your data — chat assistants, copilots and automation that resolve real work, with evaluation and guardrails built in.
Explore LLM & RAG →Automated extraction, classification and drafting over messy document sets — combining OCR, layout models and LLMs to turn paperwork into structured data and finished output.
Explore Document Intelligence →Custom ML and predictive analytics: computer vision, signal processing, identity verification, recommendation and forecasting — with secure data pipelines, MLOps and model governance for production scale.
Explore AI & ML →Reliable integrations across ERP, CRM, IAM, observability and security stacks. We standardize interfaces and improve data quality while reducing operational overhead.
Explore →Data modeling, warehousing and lakehouse architectures with governance and lineage. BI dashboards and self-service analytics that scale with your business.
Explore →Infrastructure as code, GitOps and platform engineering for faster delivery with higher reliability. Golden paths and developer platforms your teams love.
Explore →Secure SDLC, SAST/DAST, SBOM, and compliance with ISO, SOC and GDPR. Security embedded into the development process with continuous assurance.
Explore →Tailored web and API platforms with modern stacks, security best practices and CI/CD. We emphasize maintainability, observability and performance.
Production AI systems delivered by our engineering team. Client names withheld under NDA; sectors shown to indicate context. See full case studies →
Four LLM products for an immigration-tech company: automated visa memorandum drafting from raw document sets (80% of routine drafting automated), workflow optimization for case managers, client-chat SLA monitoring, and an FAQ assistant for support.
Multi-stage verification pipeline: document authenticity checks, face matching, liveness detection and behavioral analysis — replacing slow manual review that was losing customers and letting fraud through.
Adaptive IIR/FIR filter correction driven by machine learning for a global automotive manufacturer, built to keep voice commands accurate over engine and road noise. In final pre-production testing for a new vehicle line.
Real-time ML moderation flagging violations at 92% precision, plus an LLM support bot trained on the platform's knowledge base that resolves three quarters of tickets without a human.
End-to-end re-voicing pipeline: source-track analysis, context-aware translation, speech synthesis imitating the original speaker's timbre and emotion, and automatic sync with the video.
Geospatial ML over population density and transport accessibility, genetic-algorithm placement optimization, and digital-twin simulation to validate the plan before a single office moved.
Most software shops guess at infrastructure. We distribute it. AI projects come with right-sized GPU hardware — H200 NVL nodes, DGX Spark dev boxes, or full clusters — quoted in the same proposal, delivered on the same contract.
Building AI that acts in the physical world — robots, edge devices, actuation? That same model-plus-hardware approach extends to Physical AI →
Yes. Haink is a software and AI development company with a senior-only engineering team that ships production LLM applications, retrieval-augmented generation (RAG) systems, AI agents, document intelligence and custom machine learning models — not prototypes.
LLM applications and RAG, document intelligence, custom machine learning and computer vision, enterprise integrations (ERP/CRM/IAM), data and analytics platforms, DevOps and platform engineering, and security and compliance engineering.
Yes. Haink deploys LLM and ML systems on private, on-premises infrastructure for data that cannot leave a customer's network, and supplies right-sized GPU hardware in the same contract — a single accountable vendor for the model, the pipeline and the hardware it runs on.
Most engagements reach first working results in 2–4 weeks, starting with a discovery and architecture phase before iterative delivery with CI/CD, observability and security gates.
Yes. As an AI infrastructure supplier, Haink quotes right-sized GPU hardware — from DGX Spark dev boxes to H200 NVL nodes and full clusters — in the same proposal as the software, sized to measured throughput rather than vendor guesswork.
Let's shape a clear plan with milestones, architecture options and an implementation roadmap.