LLM Application Development Services

Cost tracks with complexity, integrations, and whether fine-tuning is needed. A focused single-use-case app typically runs $15,000–$40,000; a multi-feature production application with RAG, integrations, and full evaluation and guardrails runs $40,000–$120,000+. We usually start with a fixed-price PoC (typically $12,000–$20,000) that proves value on your real data before you commit to the full build, and every engagement opens with a free 30-minute call to scope and estimate honestly.

A production-ready LLM application typically takes 10–14 weeks: roughly 2 weeks of discovery and design, 5–6 weeks of model/prompt foundation and build, 2 weeks of evaluation and guardrails, then pilot and production. A scoped PoC runs in about 6 weeks. The two biggest variables are how ready your data is and whether fine-tuning is required — both of which we settle in discovery so the timeline you're given is real.

Most use cases are solved with strong prompting plus RAG, and we exhaust those first, because fine-tuning adds data work, cost, and ongoing maintenance. We reach for fine-tuning (LoRA/QLoRA, instruction tuning) when accuracy or output-format consistency hits a ceiling prompting can't clear, or when it lets us move to a smaller, cheaper model without losing quality. It's a decision made on benchmarked evidence against your evals, never on preference.

It comes down to your constraints. Hosted API models (GPT, Claude, Gemini) usually give the best accuracy per dollar and the fastest start. Self-hosted open-weight models (Llama, Mistral, Qwen) are the answer when data can't leave your infrastructure, or when high volume makes a fine-tuned smaller model far cheaper to run. We benchmark both against your accuracy, latency, cost, and data-residency requirements and document why we landed where we did — and we build so you can switch later without a rebuild.

Nobody eliminates hallucination, so we engineer it down and box it in: RAG grounding so answers come from your source documents with citations a user can check; structured-output validation against a schema; confidence thresholds that route shaky answers to human review; and hallucination-specific cases in the eval suite. We agree an acceptable error rate for the use case up front and monitor it in production — so the question becomes 'is it within the limit we set?', which is one you can actually answer.

With an evaluation framework built before the app ships — it's the least glamorous deliverable and the most important. We assemble a labelled test set of real inputs and expected outputs, then score accuracy, output validity, retrieval quality (for RAG), and hallucination rate against it. Every prompt, model, or retrieval change is re-run on the suite, so improvements are provable and regressions are caught before users see them. Quality stops being a feeling and becomes a number you can move.

Inference cost is an architecture decision, not a line item you optimise later. We route easy requests to cheap models and reserve the expensive one for genuinely hard cases, cache what repeats, compress prompts, and — where it pays off — fine-tune a smaller model. Cost-per-request is modelled during the build and watched in production like any other metric, so spend stays predictable as volume grows instead of becoming the reason finance switches the product off.

Yes — secure access to your data is core to most LLM apps. We use least-privilege access, keep data inside your defined perimeter (including self-hosted/VPC deployments when required), avoid sending sensitive data to third-party models where policy forbids it, and log every access. We also treat all user and document content as untrusted and test against prompt-injection attempts designed to override the app's instructions or exfiltrate data — with input filtering, output validation, and strict separation of instructions from content, enforced in the runtime rather than asked for in a prompt. This satisfies most enterprise security reviews and aligns with ISO 27001 controls.

Yes to both. We connect LLM apps to Salesforce, HubSpot, SharePoint, SAP, Oracle, ServiceNow, Jira, and most enterprise platforms via their APIs, plus your databases and document stores; for systems without clean APIs we build structured wrappers. And because embedded assistants get far higher adoption, we surface the app where your users already work — inside your own product UI, or via Microsoft Teams, Outlook add-ins, and SharePoint — all driven by the same backend whether it runs standalone or embedded.

Yes — and it matters, because LLM apps drift as providers update models and as inputs change, so an app accurate at launch can degrade quietly. Every engagement includes 90 days of post-launch support (monitoring, eval tracking, prompt tuning, minor adjustments), with the evaluation suite running against production to catch regressions and re-validate before any new model version is adopted. Beyond that, retainers cover model-update validation, drift monitoring, new features, and integration maintenance as your systems and the model landscape move.

Completely. All code, prompts, fine-tuned model weights, evaluation sets, and documentation are yours, unconditionally. We sign NDAs before any discovery conversation, retain no client data after a project concludes, and never use your data to train models for anyone else. For stricter requirements we work inside your cloud environment so we never hold your production data at all — you're left owning the app and the data advantage underneath it.