LLM-powered applications
Develop interfaces and APIs for summarization, extraction, classification and conversational workflows. Define output contracts and validate structured responses before downstream use.
LLM engineeringLanguage models.
Language models.
Enterprise engineering.
LLM application development that connects model capability with dependable software. Design the complete experience across prompts, context, tools, evaluation and operations.
LLM engineeringChoose the model.
Choose the model.
Engineer the behavior.
A language model offers broad capability. An enterprise application has a specific job, a particular set of users and clear boundaries. LLM engineering closes the gap between those two realities.
We help compare model options against representative tasks and practical constraints. The evaluation considers output quality, data-handling requirements, response time, context needs and expected cost rather than relying on a single general-purpose benchmark.
The surrounding application matters just as much. Prompts, retrieved context, structured outputs, tool permissions and error handling determine whether the system can perform its intended role within an existing business process.
Applications
Build around a meaningful task.
The examples below illustrate possible engagement areas. Each initiative is scoped to your organization’s requirements.
Model & prompt evaluation
Create representative task sets and compare candidate configurations. Version prompts and test changes against the same acceptance criteria so improvements can be examined.
Controlled tool workflows
Connect models with approved functions and business APIs. Validate tool arguments, restrict permissions and enforce required approvals before consequential actions execute.
The engineering approachMake the foundations
Make the foundations
part of the product.
Select with evidence
Compare hosted and deployable model options against the task. Assess quality, integration, latency, data-handling terms and operating cost in context.
Build the application layer
Design prompts, context assembly, output validation and tool boundaries. Account for rate limits, timeouts, retries and useful fallback behavior.
Evaluate changes
Test representative tasks, edge cases and adversarial inputs. Use automated checks and domain review, then record the evidence behind a release decision.
Operate the system
Monitor failures, latency and cost. Define how model versions, prompts and dependencies are changed, tested and rolled back when necessary.
Define successMeasure what matters
Measure what matters
in your environment.
A useful evaluation asks whether the application can perform the task under realistic conditions. We help establish acceptance criteria and make trade-offs visible before a broader release.
Example application: service request preparation
A service application uses an LLM to summarize an incoming request and propose structured fields. Validation checks the response against the required schema. A user reviews the proposed record before an approved API creates it. Monitoring distinguishes model errors from integration failures so the support team can investigate effectively.
Illustrative application, not a client case study.
Task acceptance
Does the output meet the agreed criteria for its intended use?
Output validity
Do structured responses satisfy their schema and business validation rules?
Service behavior
Are failures, retries and fallback paths handled predictably?
Economics at usage
How do input size, output length and traffic affect latency and cost?
The people behind the solutionAdd the expertise
Add the expertise
your team needs.
Already leading the initiative internally? We can focus the engagement on recruiting the specialists who complement your existing team.
Explore specialist staffing- LLM application engineer
- AI solution architect
- Backend engineer
- Platform engineer
- AI evaluation specialist
One initiative. The right engagement.
Discuss an individual placement, contract expertise or a scoped development effort. Responsibilities, deliverables and working arrangements are agreed before the engagement begins.
Common questions
Go deeper into LLM engineering.
When is fine-tuning appropriate?
Consider it when a well-defined behavior or task can benefit from suitable training examples and simpler approaches have been evaluated. It is not a substitute for current enterprise knowledge or for sound application design.
Should we use an open or hosted model?
The answer depends on the task, quality requirements, deployment constraints, data-handling needs and operating capabilities. We compare options against the actual use case rather than assume one model category is always preferable.
How do you manage model changes?
The delivery scope can include versioned prompts and configurations, regression evaluation, staged rollout and rollback procedures. Ownership and release criteria are agreed for the application.
Can an LLM take actions in our business systems?
It can propose tool calls when the application exposes them. The application must enforce authorization, validate inputs and require approval where the workflow demands it. The model should not be treated as the access-control boundary.
Let’s define what useful AI looks like for you.
Start with a business task, a data challenge or a specialist role. We’ll help shape the next step.