Custom AI Services tailored to your roadmap.
End-to-end AI consulting, architectural scoping, rapid prototype validation, and custom model engineering for enterprise organizations.
Generic AI lifts everyone equally. Your competitive edge comes from the custom layer.
Off-the-shelf APIs level the playing field for your competitors. Real competitive moat is won in the proprietary data you train on, the operational workflows you wire together, and the private guardrails you build around them. We design AI systems that run on your unique conditions — then ship them end-to-end, from architecture through production operations.
Problem-first scoping
We start from the core business decision, not raw technology novelty. Every engagement is framed around measurable bottom-line velocity.
Production from day one
Architectures engineered for low latency, zero data leakages, and deep audit observability with internal enterprise teams.
Your IP, fully transferred
Trained models, custom pipelines, and orchestration code ship directly with the engagement. Zero vendor lock-in and zero surprise renewals.
Six disciplines, one delivery team.
Custom AI engineering is rarely a single discipline in isolation. It combines vector retrieval with deterministic rules, lightweight predictive scoring, and rigorous MLOps telemetry to ensure zero silent model degradation over time.
AI strategy and roadmap
Turn broad mandates into a sequenced execution plan — use cases scored by business value, risk, and feasibility. You leave discovery with an actionable 12-month engineering roadmap.
Machine learning systems
Custom predictive models trained strictly on your operational data — classification, regression, forecasting, ranking. Shipped complete with evaluation harnesses and drift alerts.
Natural language pipelines
Text extraction, entity classification, summarization, and retrieval tuned to your domain vocabulary. Works reliably where generic off-the-shelf LLM prompts fall apart.
Computer vision
Detection, segmentation, and OCR for documents, production lines, and imagery — deployed at the edge, on-device, or in the cloud depending on your latency constraints.
Generative AI features
LLM-backed features wired into product surfaces — autonomous assistants, copilots, content generation, and enterprise RAG systems grounded strictly in your verified corpus.
MLOps and observability
The engineering foundation that guarantees reliability. Automated CI for models, canary deploys, feature stores, evaluation gates, and operational telemetry dashboards.
From discovery to production, one team throughout.
No handoffs between sales, consulting, and engineering. The people who scope the problem at Steigern Tech are the people who ship the system — and the people you call when it needs tuning eighteen months later.
Discovery workshop
Two-day working session with your product and data leaders. We map decision workflows, calculate feasibility and business value across candidate use cases, and isolate the highest-ROI problem to validate first.
Data diligence
Audit real production data — real-world distributions, schema fidelity, labeling quality, and security constraints to define the fastest path to a defensible, high-accuracy training dataset.
Architecture design
Model selection, serving topology, and system integration contracts tailored specifically to your latency, cost, and compliance boundaries — never an off-the-shelf generic reference template.
POC in weeks
A working prototype operating on your live data and evaluated against transparent operational baselines in two to four weeks. Includes honest architectural assessment — even when a simpler system is the optimal answer.
Production hardening
Enterprise security reviews, load testing, latency profiling, automated failover, and telemetry instrumentation. The system deploys with robust seams that your internal team can operate smoothly 24/7.
Continuous improvement
Automated feedback loops, scheduled drift tracking, and quarterly performance calibrations. Because production AI systems experience silent data decay over time, our architectures proactively flag drift before users feel it.
Discovery workshop:Two-day working session with your product and data leaders. We map decision workflows, calculate feasibility and business value across candidate use cases, and isolate the highest-ROI problem to validate first.
production deployment hardened for security, scale, and compliance
model reviews, drift analysis, and continuous improvement cycles
Outcomes that stick in production.
Pilots are easy; production outcomes are rare. These are the measurable results we bring to engagements — and the operational habits that keep them alive.
Move from pilots to production
Most AI initiatives stall at pilot. Our engagements ship — because we scope around deployable slices, not academic exercises.
Compound learning
Every operational interaction enriches your training data. The system gets sharper over time, widening your competitive moat.
Sub-second response baselines
High-concurrency inference topologies sized strictly to production throughput requirements with intelligent caching.
Total data sovereignty
Full on-premises or private cloud tenant deployment with zero telemetry leakage to public foundation model providers.
Predictable operational costs
Strict token budgeting, smart caching, and small domain models cutting monthly inference fees by up to 60%.
Seamless internal handover
Comprehensive runbooks, architectural contracts, and telemetry dashboards your internal engineers can operate 24/7.
Tools chosen for longevity, not novelty.
We're pragmatic about frameworks. The stack below covers high-throughput serving, enterprise orchestration, and local inference without vendor lock-in. See our full AI tech stack breakdown.
Idea to Market–Ready Product
We turn your idea into a secure, compliant, and test-ready product in weeks, not months.
Inquiry & Scoping
We rapidly decompose requirements, pressure-test commercial viability, and align technical scope before capital is committed.
01 / 06
Ready to build custom AI
that does real work?
Share your operational bottlenecks, dataset needs, or automation goals. An AI solutions architect will provide an architectural roadmap within 24 business hours.