Technology · AI / LLM
AI & LLM Engineering
LLMs · RAG · Agents · Evaluation harnesses
An LLM feature is easy to demo and hard to operate: the gap between the two is retrieval quality, evaluation, guardrails and cost control. That gap is precisely what we sell. We treat the model as a component in a system — never the system — and engineer everything around it so the impressive demo is still impressive with real users and a real invoice.
How TrivialWorks uses it.
Two ways. We build LLM-backed products — retrieval over your data, agents that use tools, copilots inside workflows — with an evaluation harness standing between every prompt change and production. And we run our own delivery through AI: the six-stage pipeline described on How we build, where model output ships only after machine and senior-human review. We operate what we recommend.
Decision guide
Should your project use AI & LLM Engineering?
Practical selection guidance — the conversation we would have with you before writing a line of code.
When it’s the right choice
- Products where language is the interface — support deflection, document intelligence, internal copilots over institutional knowledge
- Workflows with expensive human toil reading, drafting or triaging text at volume
- Agentic automation with real tools — booking, querying, filing — where the outcomes are checkable
- Teams that want AI capability inside existing systems, not a rewrite (that is AI Integration)
When it isn’t
- Deterministic problems — pricing rules, eligibility calculations — where correct code beats a probabilistic model every time, at a millionth of the cost
- Products whose error tolerance is zero and whose outputs cannot be checked — if a wrong answer is a lawsuit and no human reviews it, an LLM does not belong in the loop
- AI for the press release: retrofitting a chatbot onto a product that needed a better search box
Consider Plain automation — The workflow is rules all the way down — a queue and a cron job are cheaper and never hallucinate.
Best use cases
Where AI & LLM Engineering makes practical sense.
Retrieval over your own data
RAG done properly: chunking and embedding tuned to the corpus, retrieval evaluated against golden questions, sources cited so answers can be audited.
Agentic workflows
Models that call tools — search, book, file, update — inside guardrails, with every action logged and the blast radius of a bad decision bounded.
Internal copilots
Assistants over institutional knowledge for support, sales and operations teams — grounded in your documents, honest when they do not know.
Document intelligence
Extraction, classification and summarisation across contracts, claims and filings, with confidence thresholds routing the uncertain cases to humans.
Proof
Where we’ve shipped it.
Agentic platform for cross-border marketplace selling
Agents that scout Indian brands missing from foreign marketplaces, then automate their path onto Amazon UAE, US and UK — onboarding to ad campaigns. In build now.
Read the case studyConversational AI engineering: agents that cannot afford to guess
Offshore engineering team inside a US conversational-AI company — RAG-grounded agents working under HIPAA, SOC 2, GDPR and CCPA.
Read the case studyRelated services
Services that commonly use it.
Questions
AI & LLM Engineering, asked straight.
Which model providers do you work with?
The frontier providers and, where data residency demands it, self-hosted open-weight models. The architecture keeps the model swappable — the evaluation harness, not habit, decides which model earns each workload, and re-decides as the market moves.
How do you stop an LLM feature hallucinating at users?
Layers, not hope: retrieval grounding with cited sources, output validation against schemas, confidence routing to humans for the uncertain cases, and an eval suite that every prompt and model change must pass before deploy. The failure mode is engineered against, not apologised for.
What does an LLM feature cost to run?
A design decision, not a surprise. We set cost and latency budgets per feature up front — caching, model tiering and prompt discipline against them — so unit economics are known before launch, not discovered on the first invoice.
Is our data used to train someone else's model?
No. Commercial API tiers with training exclusions, or self-hosted models where the data cannot leave at all — the boundary is contractual and architectural, and we put it in writing.
Thinking about AI & LLM Engineering?
Send the requirement and you get back a functional specification — screens, data model, stack and an estimate — at no charge. If AI & LLM Engineering is the wrong choice for it, that will be in there too.