advanced ai engineering

From AI prototypes to cost-efficient production systems

Production-grade AI Engineering & Operations for teams running AI in real workflows

Claim your free assessment
See how it works

Working with Resolute on our AI initiatives was a great experience - the team was responsive, knowledgeable, and delivered exactly what we needed. The outcomes have been solid and made a real difference for us.

Brandon Satrom
SVP of Product & Experience, Blues
Aerospace

Equipping a tracking platform for AI: Secure, local RAG prototype with .NET and Semantic Kernel

Resolute built a secure, local Retrieval-Augmented Generation (RAG) assistant for an aerospace monitoring platform, enabling accurate, citation-backed answers from thousands of regulatory documents, fully developed in .NET with Semantic Kernel.

Read the full story

AI that works at scale

We help organizations run AI inside real business workflows with predictable costs, measurable quality, and faster task completion once AI moves beyond demos.

the pain points

Why AI breaks after the demo

Most AI initiatives look successful at first. But once real users, real data, and real volume enter the picture, they often become risky, expensive, or unreliable.

AI costs grow faster than usage 

As adoption increases, token usage, retries, and context size compound. Without visibility into cost drivers, AI spend quickly becomes unpredictable.

Quality degrades over time

Prompts drift, data changes, and model updates introduce silent regressions. Outputs still work but become less accurate and consistent.

Changes introduce regressions

Small updates to models, prompts, or configurations can alter behavior in subtle ways. Without systematic regression testing, issues surface only after users complain.

Agents behave unpredictably and slow teams down

As autonomy increases, agents may take incorrect actions or follow flawed reasoning paths. Instead of accelerating work, teams spend time correcting outputs, rerunning prompts, or switching tools.

Testing doesn’t scale

Manual or “vibe-based” testing catches only obvious failures. It doesn’t scale, and it misses problems before they reach users.

Cost → outcome is unclear

Teams see total AI spend, but not cost per resolved task, customer issue, or business action, making responsible scaling difficult.

How Resolute solves it

what resolute actually does

AI you can operate. Not just deploy.

Most AI partners focus on building AI features and measure success at launch. Resolute focuses on what happens after.

AI systems are engineered to remain reliable as usage grows, data changes, and models evolve, while lowering cost per task and improving workflow speed.

At the core of our model is a Forward Deployed Engineer, embedded directly into your environment: not just advising, but actively shaping how your AI systems behave in production.

  • optimizing AI systems for lower cost per task and higher throughput

  • measuring cost per business outcome, not just token usage

  • automating evaluation and regression testing to prevent productivity loss

We call this Production AI Engineering & Operations:

The discipline of making AI systems predictable, measurable, and economically sustainable once they power real business workflows.

Advanced AI Engineering

our operating model

How we engineer AI systems that hold up in production

We apply a clear, repeatable lifecycle to AI systems reliable and safe to scale.

Visibility rather than assumptions

Token-level telemetry

Track usage, latency, model choice, and cost per interaction.

Cost-per-outcome mapping

Connect AI spend to real business actions, not just token counts.

Shadow AI discovery

Identify unapproved or redundant AI usage that increases cost and risk.

Drift and usage visibility

Detect changes in usage patterns and output quality early.

Architecture that scales responsibly

Model tiering and routing

Use smaller models for routine tasks; reserve frontier models for high-value work.

Context window engineering

Reduce unnecessary prompt and context bloat to cut latency and token cost.

Inference caching

Eliminate redundant calls for semantically similar requests.

Prompt compression

Remove tokens that don’t materially improve output quality.

Governance for continuous use

Automated release gates

Quality and cost checks embedded in CI/CD pipelines. 

Prompts as code

Versioned, auditable prompts and configurations.

Usage limits and policies

Guardrails that prevent runaway cost or behavior.

Shadow deployments

Run new models or configurations in parallel before full rollout.

core capabilities

 What we build and run

These are the capabilities we implement to operate AI reliably in production, not just launch it.

AI Cost & FinOps Engineering

Designed around unit economics, not surprise bills. We architect AI systems so cost per task decreases as usage grows.

  • Token-level telemetry to understand cost drivers

  • Cost per successful task tied to real outcomes

  • Model tiering, routing, and caching to balance cost, latency, and quality.

LLM Evaluation & Experimentation

From subjective testing to engineering-grade confidence. We replace “vibe-based” validation with systematic evaluation.

  • Golden datasets built from real production usage

  • Automated LLM-as-a-Judge pipelines for quality scoring

  • Regression testing across models and versions

  • CI/CD quality and cost gates that prevent degradation

Observability & Human-in-the-Loop

Visibility and control in real-world usage. We make AI behavior observable and intervenable.

  • Drift and hallucination telemetry

  • Confidence-based escalation to humans

  • Supervised autonomy for agents

Advanced Agent Systems

Capable agents, with built-in guardrails. We design agent systems that act autonomously, but not unpredictably.

  • Multi-agent orchestration for complex workflows

  • Corrective feedback loops

  • Penalized reasoning paths to avoid repeated failure

Generative UI & Workflow Interfaces

AI tools people actually use. We move beyond chat interfaces to AI-driven workflows embedded in real systems, reducing manual steps and accelerating everyday work.

  • Structured JSON outputs for dynamic interfaces

  • Agent-defined UIs rendered on demand

  • Stateful, editable workflows connected to live data

See how we’re different

Why Resolute

why resolute

How we’re different from typical AI engineering partners

Most AI engineering partners are optimized for delivery.
Resolute is optimized for operation.

Most AI partners 

  • Ship AI features quickly

  • Measure success at launch

  • Rely on manual or ad-hoc testing

  • Address costs after they spike

  • Demo impressive agents

Resolute

  • Embeds a Forward Deployed Engineer

  • Engineers AI for long-term production use

  • Measures cost per business outcome

  • Automates evaluation and regression testing

  • Designs for predictable unit economics

  • Governs autonomy by design

Resolute brings the engineering discipline required to operate AI in regulated, compliance-heavy environments, where reliability, traceability, and auditability matter.

Suitable for organizations that:

  • Run AI in real internal or customer-facing workflows

  • Care about cost, risk, and reliability, not just experimentation

  • Are moving from pilot → production → scale

What improves after Advanced AI Engineering

Organizations typically achieve:

Lower cost per AI task

Higher AI adoption by teams

Faster response and workflow completion time

Fewer AI regressions and production incidents

If AI is becoming business-critical, it needs to be engineered like one.

For free assessment

For free assessment

Fill out the form and we’ll get back to you within one business day.
By selecting the button above, I agree to the privacy notice

Mission logged!

The Resolute crew and our design space team are charting the course to stellar AI. Expect transmission soon.

Taking the first steps into AI? Check out AI Jumpstart

Connection lost. Reconnecting...