LLM Features That Actually Ship to Production
We design, build, and operate LLM-powered features that survive contact with real users — RAG that retrieves the right thing, agents that don\u2019t loop forever, prompts that hold up under load, evals that catch regressions, and a cost line on the dashboard that doesn\u2019t double overnight. Not another impressive demo. Real features, in production, with guardrails, monitoring, and a hard answer to \u201cwhat does this cost per request?\u201d
You don't need another impressive demo.
You need a feature that works at 3am.
Most LLM projects die between the demo and the launch. The demo is magic — five hand-picked queries, one happy reviewer, one excited founder. Then production hits: ambiguous user input, latency spikes, retrieval that pulls the wrong document, hallucinations on names, prompt-injection attempts, a $9K OpenAI bill on day three, and no way to tell if the latest prompt change made things better or worse. We build for the production reality: evaluated, monitored, budgeted, and boring in the best possible way.
Demo magic, production disaster
The investor demo went perfectly. Then real users showed up, asked questions the prompt engineer never anticipated, and the model started confidently making things up. Now half the team wants to rip it out.
The OpenAI bill ate the runway
A naive RAG pipeline retrieves 50 chunks per query, sends them all to GPT-4 Turbo, and burns $0.40 per request. Multiply by 100K queries a month and your AI feature is the most expensive thing the company ships.
No way to tell if changes are improvements
You tweak the prompt. It feels better. You ship. A week later support tickets go up. Was it the prompt? The model upgrade? The new chunking strategy? Nobody knows because there are no evals, just vibes.
What You Actually Get
No vague deliverables. Here's exactly what lands in your hands.
An eval suite you trust
Golden datasets, automated grading (LLM-as-judge + deterministic rules), regression tracking on every prompt and model change. You'll know within 5 minutes whether a change is an improvement or a regression.
A RAG pipeline that retrieves the right thing
Smart chunking, hybrid search (BM25 + vector), reranking, query rewriting, citation tracking. Tuned against your data, not a generic blog post.
Cost & latency on a dashboard
Per-request, per-feature, per-customer cost tracking. Token usage, cache hit rates, model fallback rates, p50/p95/p99 latency. A monthly budget you set and we hold to.
Guardrails that hold up
Prompt-injection defenses, PII redaction, output validation, refusal handling, jailbreak monitoring, and a kill-switch for when a model upgrade goes sideways.
A Real Applied AI Team
LLM features in production take more than one prompt engineer with a Notion doc. Six roles you get on every Pillai Infotech LLM engagement.
Applied AI Lead
Picks the right model for each task, designs the prompt + retrieval architecture, sets the eval bar, and owns the cost and latency budgets. The person who says "no, we don't need GPT-4 for this".
RAG / Retrieval Engineer
Embeddings, chunking, hybrid search, reranking, query rewriting. Knows the difference between "vector search works on the demo" and "retrieval is at 92% recall on real queries".
Eval Engineer
Builds the golden datasets, designs the rubrics, wires LLM-as-judge with proper calibration, runs regression suites in CI. The person who turns "feels better" into a number.
AI Safety & Guardrails Engineer
Prompt injection defenses, PII handling, output validation, jailbreak monitoring, refusal calibration. Reads the OWASP LLM Top 10 for fun.
AI FinOps Engineer
Per-request cost tracking, prompt caching, semantic caching, model fallback strategies, batch APIs where they fit. Cuts bills by 50–80% on rescue projects.
MLOps / Production Engineer
Wires the inference layer, the queueing, the retry logic, the monitoring, the canary releases, and the rollback. Treats LLM features like any other production system, not a magic black box.
You See Everything. In Real Time.
Every Pillai Infotech project comes with a dedicated client dashboard. Kanban boards, live logs, test results, meeting notes — it's all visible the moment it happens. No status-report theatre, no "we'll get back to you", no surprises at the demo. You work with us like you work with your own team.
Kanban Board, Live
Every epic, every story, every task — visible on your dashboard. Drag, comment, reprioritize. It's the same board our team works from.
Documented Everything
Every decision, spec, API contract, and architecture diagram lives in the dashboard. Searchable, versioned, linked to the tasks they shaped.
Live Logs & Test Results
Build logs, deployment logs, test suite results — streamed to your dashboard the moment they run. You never have to ask "did the build pass?"
Meetings → Tasks, Automatically
Every meeting is recorded, transcribed, and every action point is auto-converted into a tracked task assigned to the right person. Nothing gets lost between calls.
Sprint Burndown & Velocity
See exactly how much work is done, how much remains, and our velocity over time. If a sprint is slipping, you see it the same moment we do.
Comment, Approve, Decide — In-Place
Comment on any task, approve designs, sign off on specs, and raise blockers directly in the dashboard. Everything tied to the work, not buried in email threads.
LLM Features We Know How to Ship
We pick the architecture to match the use case. Not every feature needs an agent, and not every chatbot needs RAG.
💬 Customer-facing chat & assistants
Domain-grounded chat with RAG, conversation memory, tool calls, citations, escalation to human, and the moderation layer to keep it on-brand and on-policy.
📚 RAG over your documents
Ingest, chunk, embed, retrieve, rerank, generate. Works on PDFs, Notion, Confluence, SharePoint, Google Drive, Slack — whatever you actually have.
🤖 Agentic workflows
Multi-step agents that call tools, query APIs, browse, and chain reasoning. With loop limits, cost ceilings, and human-in-the-loop checkpoints. Not a runaway demo.
📝 Content generation pipelines
Drafts, summaries, translations, structured extraction. Eval-driven, with versioned prompts, batch APIs, and a human review queue where it matters.
🔎 Semantic search & classification
Embedding-based search, intent classification, deduplication, tagging. Often the right answer when "agent" is the wrong one — and a fraction of the cost.
🩺 LLM rescue engagements
Your LLM feature is in production, the bill is too high, the quality is too low, and nobody knows why. We come in, instrument it, fix the leaks, and write the eval suite.
The LLM Stack We Use
Model-agnostic by design, opinionated by experience. We pick the cheapest model that passes the evals.
Models & Providers
Retrieval & Vectors
Frameworks & Orchestration
Eval & Observability
A Six-Stage LLM Delivery Process
Built around the reality that LLM features fail differently than normal software — silently, expensively, and at scale.
Use-Case & Feasibility Check
What problem are we solving, who for, and is an LLM actually the right tool? Sometimes the answer is "use a regex" or "use a classifier". We'll tell you when it is.
Eval First, Prompt Second
We build the eval set before we build the feature. Golden examples, edge cases, adversarial inputs, success criteria. You can't improve what you can't measure.
Architecture & Model Selection
RAG vs fine-tune vs prompt-only. Which model. Which retrieval strategy. Cost and latency budgets in writing. Trade-offs documented.
Build, Eval, Iterate
Tight loop: change the prompt or retrieval, run the evals, see the delta, ship or revert. No vibes. Every change tracked, scored, versioned.
Productionize & Guardrail
Inference layer, queueing, retries, fallback models, prompt-injection defenses, PII redaction, monitoring, kill switch. Treat it like any other production system.
Ongoing Eval & Cost Review
Monthly review of eval scores, cost per request, latency, drift. Model providers ship upgrades — we re-run evals before adopting them, not after.
Three Ways to Engage
LLM work comes in different shapes. Pick the engagement that matches your stage.
AI Feasibility Sprint
Two-week fixed engagement to validate your use case, build a thin prototype, design the eval set, and produce a real cost / quality / timeline estimate.
- Use-case validation
- Working prototype + eval set
- Honest go/no-go recommendation
LLM Feature Build
End-to-end delivery of an LLM feature — from architecture to production, with evals, guardrails, monitoring, and cost dashboards.
- Fixed scope, fixed price
- Typical: 8–14 weeks
- 60-day post-launch warranty
Embedded Applied AI Squad
A dedicated AI lead + RAG engineer + eval engineer working alongside your team on your AI roadmap.
- AI lead + RAG + evals + ops
- Monthly retainer
- Best for: AI-first product teams
Honest Answers to LLM Reality Questions
The questions every smart buyer asks before signing. Here's what we tell them.
Do we need to fine-tune a model?
Almost never, at first. 90% of "we need a custom model" turns into "we needed better retrieval and a better prompt". Fine-tuning is justified when you need a specific output format, a domain vocabulary the base model doesn't know, or to compress a long prompt into a smaller cheaper one. We'll tell you which camp you're in — usually after the eval set tells us.
OpenAI, Anthropic, Google, or open-source?
Whichever passes your evals at the lowest cost and latency. We benchmark multiple providers on your actual task before recommending. Frontier models (GPT-4 class, Claude Opus, Gemini Pro) for hard reasoning. Smaller models (Haiku, GPT-4o-mini, Flash) for everything else — which is most things. Open-source (Llama, Mistral) when data residency or cost demands it.
How do you stop hallucinations?
You don't stop them — you contain them. RAG with strict grounding, citations the user can click, structured outputs validated against schemas, refusal training where the model should say "I don't know", and evals that score factuality on a regression set. Plus a UX that signals confidence honestly. Hallucinations are a probabilistic problem, not a bug to "fix".
How do you handle prompt injection?
Layered defenses. Treat all user input as untrusted, separate system instructions from user content with clear delimiters, validate outputs against schemas, scan for known jailbreak patterns, restrict tool calls to a whitelist, and never let the LLM execute arbitrary code or shell. We follow OWASP LLM Top 10 and run adversarial evals as part of the regression suite.
How much will this cost per month?
Depends entirely on volume, model choice, and prompt length — but we model it before we build it. Typical small chatbot: $50–500/month. Mid-volume RAG over docs: $1K–10K/month. High-volume customer-facing assistant: $10K–100K/month. We set a budget in the scoping call and instrument the system so you see the numbers daily, not at month-end.
Can you cut our existing LLM bill?
Usually yes — by a lot. The five biggest wins we see: model downgrade (GPT-4 → smaller model where evals allow), prompt caching (Anthropic / OpenAI), semantic caching, batch APIs for non-realtime work, and shorter prompts via better retrieval. Average cost cut on rescue engagements is 50–70% with no quality regression.
What about data privacy and our customers' data?
Use providers with zero-data-retention agreements (OpenAI Enterprise, Anthropic, Azure OpenAI, Google Vertex), enable no-train flags, redact PII before sending, log only what you need, and document the data flow for your DPIA. For strict compliance (HIPAA, GDPR-strict, regulated industries) we deploy open-source models in your VPC. We've been through the reviews.
Will it still work when the model upgrades?
Probably, but never assume. Every model upgrade goes through our eval regression suite before it reaches production. Sometimes a "smarter" model is worse on your task — we've seen it. We pin model versions in production and upgrade on a schedule, not when a provider's blog post tells us to.
How do you know if the LLM feature is actually useful?
Two layers. Offline: eval scores against the golden set. Online: product metrics — task completion rate, deflection rate, user thumbs up/down, escalation rate, retention impact. If those numbers don't move, the feature isn't useful, no matter how impressive the demo. We track both from day one.
Can you sign an NDA before we share details?
Always. NDA before the first call. Access to your data only after scoping and written approval. We don't train models on your data and we don't reuse prompts or knowledge between clients. Standard.