AI Modernization Services | LLM Integration & Enterprise AI Solutions

Q: What is AI modernization?

AI modernization is the process of integrating advanced artificial intelligence capabilities, such as LLMs, computer vision, and predictive analytics, into existing legacy systems and workflows to improve efficiency, automation, and decision-making.

Q: How long does an AI integration project take?

Project timelines vary based on complexity. A proof-of-concept typically takes 2-4 weeks, while full production implementations range from 2-6 months depending on the scope, data requirements, and integration complexity.

Q: Do you work with specific industries?

We work across multiple industries including FinTech, Healthcare, Manufacturing, Retail, and Logistics. Our methodology is sector-agnostic but we build specialized models trained on each industry's unique data and edge cases.

Q: What AI models and technologies do you use?

We work with leading AI platforms including OpenAI GPT, Anthropic Claude, Google Gemini, and open-source models. We select the right model based on your specific requirements for capability, latency, compliance, and cost.

Why teams hire us · 01

Most AI initiatives stall at the demo.

A prototype that wows the boardroom is the easy part. Getting it past security review, wiring it into a fifteen-year-old ERP, and proving it works on your real data. That is where projects die.

Aether is the production team. We audit your systems, scope a fixed engagement, and ship AI that runs inside your infrastructure, with the evals, guardrails, and documentation your auditors will ask for.

2–4 wks

To a working proof of concept

100%

Senior engineers on every project

AI economics · COST PER SUCCESSFUL TASK

More useful work per model dollar.

Token counts are an input, not the outcome. We baseline the complete workflow, then tune model choice, context, reuse, and fallback paths against the quality and latency targets that matter.

The result is a system your team can inspect: what each successful task costs, where tokens go, and which change created the savings.

See how we baseline and optimize

Illustrative routing model

61%

modeled reduction in model cost

100-step scenario

100

Baseline cost units

Every step sent through the highest-cost route.

39

Optimized cost units

Work matched to frontier, standard, or efficient routes.

Transparent normalized model, not a client result. Actual savings depend on workload, provider pricing, quality floor, latency target, and traffic shape.

Usage acceleration 320× year-over-year growth in reasoning-token use per organization reported by OpenAI. Source Efficiency gap 30× same-task token variability observed in a 2026 agentic coding study. Research

Decision metric $/success total model spend divided by workflows that clear the agreed evaluation. Quality-adjusted

The optimization system

Four levers. One measurable loop.

01 · ROUTE

Match intelligence to the work

Reserve frontier reasoning for the steps where it changes the outcome. Use efficient routes for retrieval, classification, and routine execution.

02 · SHAPE

Send less, better context

Build compact, permission-aware evidence packets instead of repeatedly loading whole files, histories, or memory stores.

03 · REUSE

Avoid paying twice

Cache safe, repeatable work and preserve approved intermediate results with clear invalidation and fallback rules.

04 · VERIFY

Protect the quality floor

Track tokens per workflow, cost per successful task, evaluation pass rate, and p95 latency before promoting a cheaper path.

Agentic systems · 08 CAPABILITIES

The operating layer behind reliable agentic work.

A modular system for experience, orchestration, compute, memory, and specialized intelligence. Start with one capability or connect the full layer.

A.01 · EXPERIENCE

Governed AI workspaces

One controlled place for teams to use approved models, company tools, and organizational knowledge under consistent identity and policy.

Unified access

A.02 · ORCHESTRATION

Agent orchestration & evaluation

Decompose goals, coordinate specialist agents, supply focused context and tools, then verify the result against a defined quality bar.

Reliable execution

A.03 · COMPUTE

Remote agent environments

Isolated cloud workspaces that preserve task state, continue after a device disconnects, and can be securely reviewed or steered.

Durable work

A.04 · MEMORY

Organizational memory systems

Capture, connect, permission, and retrieve compact source-linked knowledge so people and agents stop rebuilding the same context.

Shared evidence

A.05 · INTELLIGENCE

Private specialized models

Customer-controlled model artifacts evaluated against your tasks, language, policy, and quality requirements for stable high-volume work.

Owned economics

A.06 · WORKFLOWS

Custom agent workflows

Turn repeatable operating processes into governed agent workflows with the right tools, approvals, evidence, and human checkpoints.

Operational leverage

A.07 · INTEGRATION

Enterprise AI integration

Connect identity, data, tools, model providers, infrastructure, observability, and policy without replacing the systems you already run.

Controlled adoption

A.08 · ADAPTATION

Model adaptation & deployment

Evaluate, adapt, package, and deploy specialized intelligence with clear provenance, deployment controls, and measurable acceptance gates.

Production ownership

Services · 08 CAPABILITIES

The full stack, as a spec sheet.

Strategy, data, models, integration, and the training to run it all without us. Every line links to the detail.

S.01 AI Staff Augmentation Agents embedded in your repos and queues that code, review, and ship around the clock. S.02 Enterprise Agentic AI Autonomous workflows that plan and execute across your software, with human checkpoints where they matter. S.03 AI Customer Support Voice and text agents for call centers: instant answers, clean escalation to humans. S.04 AI Training for Teams Hands-on programs for ChatGPT, Claude, Gemini, Copilot, and developer tools like Cursor. S.05 Computer Vision & Physical AI Detection, inspection, and video analytics running in real time on factory floors and job sites. S.06 Edge AI & SLMs Small language models deployed on-premise. Low latency, zero data egress. S.07 Generative Media Synthetic data, production video pipelines, and natural-sounding voice agents. S.08 AI Governance & Compliance EU AI Act and NIST alignment, evals, and guardrails that hold up in audit.

How we engage · 04 PHASES

Every phase ends with something you keep.

Fixed scopes, staged commitment. You can stop after any phase and walk away with the deliverable.

PHASE 01 1–2 wks

Audit & Discovery

We map your systems and data, and find the highest-leverage places to put AI to work.

You keepAn opportunity map and a fixed-scope proposal.

PHASE 02 2–4 wks

Prototype

A proof of concept built against your real data, not a slide deck, to validate feasibility and ROI.

You keepA working prototype and an honest go/no-go readout.

PHASE 03 6–12 wks

Implementation

Production engineering: integration, pipelines, evals, monitoring, and security review.

You keepA system in production, documented and observable.

PHASE 04 Ongoing

Scale & Optimize

Continuous tuning as your data and business change, plus training so your team can run it.

You keepDashboards, runbooks, and a team that owns it.

The full methodology

Field reports · CLIENT OUTCOMES

“We brought them in as staff augmentation for our ML backlog. Two engineers onboarded fast and shipped a production-ready ranking model in six weeks.”

James M. · VP Engineering · B2B SaaS

“We were skeptical about AI for ops. They built a small pilot, proved impact, then scaled it. Forecast accuracy improved ~9%.”

Ravi P. · VP Operations · National Logistics

“They worked within our security constraints and documented everything. Vendor review took days instead of weeks.”

Alicia T. · Security Lead · Enterprise Software

Results vary with data quality, scope, and operational readiness.

Read a full field report · FP&A forecast automation

FAQ · 05

Common questions

What is AI modernization?+

Integrating AI capabilities such as LLMs, computer vision, and predictive analytics into the systems and workflows you already run, to improve efficiency, automation, and decision-making without replacing your stack.

How long does an AI integration project take?+

A proof of concept typically takes 2–4 weeks. Full production implementations run 2–6 months depending on scope, data requirements, and integration complexity.

Do you work with specific industries?+

We work across FinTech, Healthcare, Manufacturing, Retail, Logistics, and more. The methodology is sector-agnostic; the models are built on your industry's data and edge cases.

What AI models and technologies do you use?+

Frontier platforms (Anthropic Claude, OpenAI GPT, Google Gemini) plus open-source models. We select per project for capability, latency, compliance, and cost.

How do you reduce AI token spend without reducing quality?+

We baseline cost per successful task, token use, quality, and latency, then tune model selection, context payloads, caching, and fallback paths. Changes ship only when they preserve the agreed quality and reliability thresholds.

AI that ships inside the systems you already run.

The savings opportunity is too large to leave unmeasured.

Most AI initiatives stall at the demo.