Clear scope
The first useful release is named before seats and tooling scale.
AI & Machine Learning Solutions
LLM Development
LLM features that earn their place in the product
We build language-model workflows around real tasks, data boundaries, and human review—not demos that collapse in production.
Capability deep dive
LLM Development without the template feel
Language-model features with retrieval, evaluation, and guardrails your team can operate.
- 01
LLMs
- 02
RAG
- 03
Evals
- 04
Guardrails
Engagement overview
LLM Development with visible decisions
Language-model features with evaluation and guardrails.
We scope the boundary, success signal, and operating model before scaling implementation—so the work stays reviewable and transferable.
Who this is for
Ideal fits for llm development
- 01Teams with a concrete workflow to improve
- 02Products that need a focused first release
- 03Organizations that want clear ownership after handoff
What you get
Outcomes we optimize for
Operable delivery
Implementation includes tests, observability, and acceptance criteria.
Transferable ownership
Docs and next steps leave your team able to continue.
Measurable progress
Success signals are agreed so demos are evidence, not theater.
Technology
Tools and stacks we commonly use
Final choices follow your product constraints. These are typical foundations for llm development engagements.
- TypeScript
- React
- Node.js
- PostgreSQL
- AWS
- CI/CD
Plan the engagement
What we decide before building llm development
Resolve these boundaries early so scope and architecture follow the actual product context.
- LLMs
- RAG
- Evals
- Guardrails
- Tooling
- 01
Task definition
Name the job to be done, acceptable failure modes, and when a human must review.
- 02
Data boundaries
Decide what private data can enter prompts, retrieval indexes, or third-party providers.
- 03
Evaluation bar
Agree quality, latency, and cost thresholds before scaling usage.
Where we help
From prototype prompt to operable feature
Retrieval and grounding
Connect models to approved knowledge with clear freshness and permission rules.
Tool use and workflows
Let models call APIs safely inside constrained product flows.
Eval and monitoring
Measure quality in staging and production so regressions are visible.
How we deliver
Start with the decision that changes the build
We lock the product boundary, success signal, and ownership model before scaling implementation.
Frame the problem
Document users, constraints, integrations, and the smallest useful release.
Build the operable slice
Ship a reviewable increment with tests, observability, and clear acceptance.
Transfer ownership
Leave docs, runbooks, and next-step options your team can actually run.
Common questions
Frequently asked questions
Answers to common questions about working with SolveMotive on llm development.
Do you fine-tune models or use hosted APIs?
We choose based on data sensitivity, quality needs, and cost. Many products start with hosted models plus retrieval, then consider fine-tuning only when evidence justifies it.
How do you reduce hallucinations?
Grounding, constrained outputs, evaluation sets, confidence handling, and human review on high-risk actions are part of the design—not an afterthought.
How does this relate to the parent practice?
This page goes deeper on a specific capability. The parent practice page shows the full capability map and how engagements usually combine.
Can this be combined with other services?
Yes. Most engagements combine related capabilities under one accountable delivery plan.
Continue exploring
- Service
AI & Machine Learning Solutions
Practical AI systems designed around a defined decision, workflow, or product capability.
- Service
AI Product Development
Ship AI as a product surface: UX, evaluation, ownership, and iteration—not a bolted-on model.
- Service
Chatbot Development
Conversational assistants grounded in your content, policies, and escalation paths.
Put an LLM on a real product job
Tell us the workflow, data constraints, and what “good enough” means for your users.