Software and AI, built by a senior-only team

Production systems, not prototypes: automation, analytics, document intelligence and LLM applications. Dedicated teams per project — and we'll tell you honestly when AI is the wrong tool for the job.

What we build

01

Custom Web & API Platforms

Tailored web and API platforms with modern stacks, security best practices and CI/CD. We emphasize maintainability, observability and performance.

TypeScriptPythonGoCI/CD
02

AI & Machine Learning

Intelligent automation and predictive analytics using classical ML and LLMs. Secure data pipelines, MLOps and model governance for production scale — sized to the GPU hardware it actually runs on.

LLM appsRAGFine-tuningMLOps
03

Enterprise Integrations

Reliable integrations across ERP, CRM, IAM, observability and security stacks. We standardize interfaces and improve data quality while reducing operational overhead.

ERP/CRMIAMAPI design
04

Data & Analytics

Data modeling, warehousing and lakehouse architectures with governance and lineage. BI dashboards and self-service analytics that scale with your business.

LakehouseBIGovernance
05

DevOps & Platform Engineering

Infrastructure as code, GitOps and platform engineering for faster delivery with higher reliability. Golden paths and developer platforms your teams love.

IaCGitOpsKubernetes
06

Security & Compliance

Secure SDLC, SAST/DAST, SBOM, and compliance with ISO, SOC and GDPR. Security embedded into the development process with continuous assurance.

Secure SDLCSBOMISO/SOC/GDPR

Selected case studies

Production AI systems delivered by our engineering team. Client names withheld under NDA unless public.

Immigration tech · USA

LLM ecosystem for a talent-visa startup

Four LLM products for a US 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.

−45% case processing time+30% throughput+25% client satisfaction
Fintech · online lending

AI identity verification for a loan marketplace

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.

−75% fraudulent applications−60% verification time+35% conversion
Automotive · Hyundai

ML noise suppression for in-car voice control

Adaptive IIR/FIR filter correction driven by machine learning, built to keep voice commands accurate over engine and road noise. In final pre-production testing for a new vehicle line.

+40% recognition accuracy in noise95% positive tester feedback
Creator economy · lava.top

Content moderation and support AI for a creator platform

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.

−60% moderation time75% tickets auto-resolved−50% support load
Media · localization

AI dubbing with voice and emotion preservation

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.

10× faster localization95% translation accuracy85% emotion preservation
Public sector

Branch network optimization for a public-services operator

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.

+42% service accessibility−35% waiting time+28% citizen satisfaction
25+delivered AI projects
15+corporate clients
98%client satisfaction
2–4 weeksto first working results

The hardware advantage

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.

One contractmodel, pipeline and the GPUs it runs on — single accountable vendor
Right-sizedinfrastructure matched to measured throughput, not vendor guesswork
Private by defaulton-premises LLM deployments for data that can't leave your network
Senior-onlyno junior staffing pyramids — engineers who have shipped before

How a project runs

STEP 1
DiscoveryGoals, constraints, data audit. A clear plan with milestones and architecture options.
STEP 2
Architecture & roadmapImplementation roadmap with hardware sizing if AI workloads are involved.
STEP 3
Build & shipIterative delivery with CI/CD, observability and security gates from day one.
STEP 4
OperateMLOps, monitoring, retraining pipelines and SLAs — or full handover to your team.

Have a project in mind?

Let's shape a clear plan with milestones, architecture options and an implementation roadmap.

sales@haink.org