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LLM DEVELOPMENT SERVICES

Put a Language Model to Work Where It Earns Its Cost

A model upgrade should not force a rebuild or increase spend without proving better results. Tech.us builds LLM systems that make model changes easier to test and easier to justify.
We design the evaluation and retrieval layers around your application so quality stays measurable even when the underlying model changes.

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WHAT LLM DEVELOPMENT CHANGES

Make Model Changes Without Rebuilding the Business Around Them

A better model should improve the system, not force the team to start over. We build the application so quality can be measured and model changes can be made without rewriting everything around them.

Upgrade With Evidence

Test a new model against real work before it reaches users.

Reduce Rework

Keep the parts that matter to the business independent of any one provider.

Keep Quality Visible

Know whether a change actually improved the result instead of relying on a vendor benchmark.

Stay in Control of Cost

Choose the model based on the value of the task rather than sending every request to the most expensive option.

LLM DEVELOPMENT SERVICES

From Model Selection to a System You Can Operate

We turn the right model into a production system your team can rely on and improve over time.

Feasibility and Model Selection

We define the task, build an initial evaluation set, and benchmark candidate models against it on your data rather than on published comparisons.

Retrieval Engineering

We build ingestion, chunking, embedding, hybrid search, reranking, and citation, tuned against the queries your users actually submit.

Fine-Tuning and Adaptation

We prepare training data, fine-tune with parameter-efficient methods where they suffice, and measure whether the adapted model beats the prompted baseline.

Small Language Model Development

We build and adapt compact domain-specific models for narrow tasks where cost, latency, or deployment location rules out a hosted frontier model.

Evaluation Engineering

We build task-specific evaluation covering correctness, format compliance, refusal behavior, and regression, so model and prompt changes are measured rather than assumed.

Prompt and Output Engineering

We design prompts as versioned artifacts, constrain output to validated structures, and handle the cases where the model returns something unusable.

Guardrails and Safety Controls

We implement input and output filtering, permission enforcement, injection resistance, and defined refusal behavior around the model.

Cost and Latency Engineering

We design the routing, caching, context strategy, and model mix that determine what the system costs per task at production volume.

Private and On-Premise Deployment

We deploy open-weight and adapted models inside your environment where data sensitivity or regulatory position rules out sending information to an external provider.

Application Integration and Operations

We connect the system to your applications and data, then monitor quality, cost, latency, and failure rates in production.

Selected Work

Systems We Have Taken Into Production

See how Tech.us engineers systems around real business workflows, data, users, and operating constraints.

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AI-Powered Takeoffs From Complex Construction Drawings

A precast concrete manufacturer partnered with Tech.us to bring AI into the estimating workflow. The system uses OCR, computer vision, and generative AI to identify, extract, and visualize structures, pipes, and components from complex construction drawings.

Read the Case Study
Wellington Hamrick Precast AI takeoff automation tool shown on laptop and tablet

Mobile Fleet Visibility Built for Field Operations

SkyHawk by TELUS needed a streamlined mobile experience for its Connect Anywhere platform. Tech.us built the core experience around real-time asset tracking, fleet activity, secure configuration, and map-based operational visibility.

Read the Case Study
SkyHawk by TELUS mobile app screens showing fleet tracking and login

Personalized Financial Technology Built to Scale

Tech.us helped bring Wealth Mastery to life as a digital platform that puts personalized financial planning tools directly in users' hands while supporting a large and growing audience.

Read the Case Study
Tony Robbins Wealth Mastery app shown on tablet and phone

WHY TECH.US FOR LLM DEVELOPMENT

Built for Long-Term LLM Success

We focus on the parts of an LLM system that continue creating value as models evolve, costs change, and business needs grow.

We Build the Evaluation Set First

It is the deliverable that keeps its value when the model changes, and we hand it over rather than keeping it.

We Recommend the Cheapest Approach That Works

Fine-tuning is proposed when prompting and retrieval have been tested and fallen short, not as an opening position.

We Engineer Cost Into the Architecture

Routing, caching, and context strategy are design decisions, because cost problems discovered at scale are structural rather than tunable.

We Deploy Inside Your Environment When Required

Open-weight and compact models running on your infrastructure are part of what we build, not an exception we refer elsewhere.

We Hand Over Something Portable

The abstraction layer, evaluation set, prompts, and documentation are yours, so you are not dependent on us or on a single provider.

AI-Assisted Code Is Reviewed by an Engineer

Code written with AI assistance passes human review before it reaches a client system. Faster authorship does not move accountability.

why-choose-techus

HOW WE WORK

Build the Evaluation Set Before Building the System

We validate the right approach before investing in the wrong solution.

1

Define the Task and What Correct Means

We establish exactly what the system must produce, who uses it, and what distinguishes an acceptable answer from a plausible one. 

2

Build a First Evaluation Set

We assemble real examples with agreed answers, including the hard and ambiguous ones, before any model is selected. 

3

Benchmark Candidates on Your Work

We test viable models and approaches against that set, comparing quality, cost per task, and latency rather than general capability. 

4

Build the Simplest Version That Passes

We start at the lowest rung that meets the bar, since a working prompted system with good retrieval is cheaper to run and easier to change than a fine-tuned one. 

5

Add Controls and Instrument

Guardrails, permission enforcement, output validation, and monitoring go in before launch, not after the first incident. 

6

Operate and Re-Benchmark

We monitor quality, cost, and failures in production, and re-run the evaluation set when a new model or a change arrives. 

CHOOSING THE APPROACH

Start at the Cheapest Intervention and Stop When It Works

Each step up this ladder costs more to build and more to maintain. Most projects settle lower than the team expected.

Better prompting and structure

Clear instructions, constrained output formats, decomposed tasks, and worked examples resolve a surprising share of quality problems at no infrastructure cost.

Retrieval

Where the gap is missing knowledge rather than missing capability, connecting the model to your own material fixes it, and updating that material requires no retraining.

Fine-tuning

Warranted when you need a consistent format, tone, or task behavior that prompting cannot hold reliably. It teaches behavior effectively and is a poor way to teach facts.

A small language model

For narrow, high-volume tasks, a compact model adapted to your domain can match a large one at a fraction of the running cost, and it can be deployed where a hosted model cannot go.

Continued pretraining

Occasionally justified for genuinely distinct domains with substantial proprietary text. It is expensive, slow, and rarely the right answer for a first project, and we will say so.

MODEL PORTABILITY

Switch Models Without Starting Over

Model providers will keep changing. Your application should not have to.

We separate the business logic from the underlying model so a new option can be tested and adopted without rebuilding the surrounding system.

  1. Compare Before You Switch

    Run competing models against the same real-world examples and keep the one that performs better for your work.

  2. Keep Your Core Assets

    Your prompts, evaluation cases, retrieval logic, and application controls remain usable when the model changes.

  3. Avoid Provider Lock-In

    A provider change becomes a controlled engineering decision instead of a new implementation project.

QUALITY YOU CAN PROVE

Know Whether a Change Actually Made the System Better

LLM output can look convincing even when quality has gone backwards. We test changes against agreed examples from your business so improvement is measured rather than assumed.

Test Real Tasks

Evaluate against the work users actually expect the system to handle.

Catch Regressions Before Release

A prompt or model change should not quietly improve one task while breaking another.

Turn Failures Into Future Tests

Once a bad result is identified, it becomes part of the evaluation set so the same problem does not return unnoticed.

COST CONTROL

Keep LLM Spend Tied to Business Value

Production cost is shaped by how the system is designed, not only by which model has the lowest price.

Use Expensive Models Only Where They Earn It

Route simpler work to lower-cost options and reserve stronger models for tasks that need them.

Reduce Unnecessary Context

Better retrieval keeps requests focused so the system sends less information on every call.

Measure Cost per Useful Result

We evaluate what it costs to complete the task successfully, not just the price of a single model request.

DATA & DEPLOYMENT CONTROL

Keep Sensitive Work Where It Belongs

Different workloads have different limits on where information can be processed. We design that boundary before the system goes live.

Send Only What the Task Needs

Limit the information that reaches the model instead of sending complete records by default.

Keep Sensitive Work Inside Your Environment When Needed

Use private or on-premise deployment where external processing is not acceptable.

Control What Users Can Receive

Apply permissions to the information retrieved and to the answer returned so sensitive content does not reach the wrong user.

Keep Outputs Reviewable

Record enough context around important responses to understand how they were produced later.

WHAT CHANGES

What Working LLM Infrastructure Makes Possible

See how a well-built LLM system creates lasting value beyond the first deployment.

Model Upgrades Become Routine

With an evaluation set in place, a new release is tested in a day and adopted or rejected on evidence rather than debated.

Quality Improves Without Retraining

Most gains come from better retrieval, clearer structure, and validated output, all of which can be changed continuously.

Costs Stay Attached to Value

Routing and caching mean spend concentrates on the requests that justify it rather than scaling uniformly with usage.

Sensitive Work Becomes Available

Tasks that were ruled out because data could not leave the environment become feasible when a compact model can run inside it.

Failures Become Explainable

Recorded versions, prompts, and retrieved sources mean a bad output can be reconstructed rather than explained away.

LLM TECHNOLOGY

The Stack Follows the Constraint

We work across commercial and open-weight model families, parameter-efficient fine-tuning, embedding and vector retrieval, reranking, serving and inference optimization, evaluation frameworks, guardrail tooling, and observability, on major clouds and on-premise infrastructure. Selection follows measured quality on your evaluation set, cost per task at your volume, latency, context requirements, where data is permitted to be processed, and what your team can operate without us.

Agentic AI & Orchestration

Agentic AI Agentic AI
AI Agents AI Agents
Multi-Agent Systems Multi-Agent Systems
Agentic Workflow Automation Agentic Workflow Automation
Model Context Protocol (MCP) Model Context Protocol (MCP)
Agent-to-Agent Protocol (A2A) Agent-to-Agent Protocol (A2A)
Agent Memory and Reasoning Agent Memory and Reasoning

FAQ

Questions Buyers Ask About LLM Development

Adapt an existing one in almost every case. Training a model from scratch is a research programme with a budget to match, and adapting an open-weight or commercial model reaches a better result for a fraction of the cost.

Retrieval when the gap is knowledge, fine-tuning when the gap is behavior. Fine-tuning is an effective way to teach format, tone, and task structure and a poor way to teach facts, since facts change and retraining does not.

For narrow, repetitive, high-volume tasks, and wherever the workload cannot leave your environment. A compact model adapted to a specific job frequently matches a much larger one on that job at a small fraction of the running cost.

By measuring cost per completed task rather than per call, keeping context small through better retrieval, caching what repeats, and routing easy requests to cheaper models. Cost is designed in, because at volume it becomes structural.

By evaluating across repeated runs rather than once, grading against criteria the answer must satisfy instead of exact text, validating output structure programmatically, and running the full evaluation set on every prompt or model change.

It is a collection of real tasks from your work with agreed correct answers. It is what lets you tell whether a new model, prompt, or retrieval change is genuinely better, and without one every upgrade decision is guesswork.

That depends on the provider and the contract, including retention, training use, subprocessors, and region. We establish what applies to your account specifically, and where the answer is unacceptable we build with models that run inside your environment.

Yes. We deploy open-weight and adapted models on-premise or in your private cloud, which is often the practical route for regulated or sensitive workloads.

Permission checks are applied to what can be retrieved and to what can be returned, rather than only at sign-in. Retrieved content is treated as untrusted, since instructions can be embedded in documents, and what the system is allowed to do is bound accordingly.

Not if the system is built for it. Model calls route through a layer you control, and the evaluation set makes switching a measurement rather than a leap, so provider changes stay configuration work.

Per project, driven by task complexity, the state of the source material, whether adaptation or private deployment is required, integration depth, and whether we operate the system afterwards.

Ready to Move Past the Prototype?

Tell us the task and the volume you expect. We will benchmark the realistic options on your own examples and give you quality and cost per task before anything is built.

Schedule an LLM Feasibility Review

Evaluation set · Approach recommendation · Cost per task · Practical next step