US-based LLM development services from a senior AI development company — production LLM apps, RAG, agents, function calling, and fine-tuning on OpenAI, Anthropic, Azure OpenAI, Bedrock, and open-source models.
Retrieval for facts, fine-tuning for behavior. Retrieval-augmented generation keeps answers grounded in current documents and lets the system cite sources; fine-tuning is the right tool only when you need a consistent output format, tone, or a narrow classification task. Most teams that fine-tune first end up rebuilding on retrieval. NextGen starts with retrieval and adds fine-tuning only where an evaluation proves it earns its keep.
Start with prompting plus retrieval — it is faster, cheaper, and far easier to change when requirements shift. Fine-tune when you have hundreds of high-quality examples, a stable task definition, and an evaluation showing prompting has plateaued. Fine-tuning too early locks in behavior you will want to change and adds a retraining cycle to every product decision.
Four levers, in order of impact: route easy requests to a smaller model, cache aggressively on repeated context, cap tokens and retries per request, and track spend per tenant so a single heavy user cannot silently absorb the budget. NextGen instruments cost per request from the first deploy, because an LLM feature without cost telemetry is a bill waiting to surprise you.
With a labeled evaluation set built from real user cases, scored on task success rather than on model preference, and run automatically before every deploy. Vibes-based QA is why LLM features regress silently after a prompt change. NextGen builds the eval harness alongside the feature, so quality is a number that either holds or fails the build.
LLM development services are the engineering discipline of designing, building, evaluating, and operating features powered by large language models. NextGen Coding Company is a US-based LLM development company that ships production LLM apps — retrieval-augmented generation, multi-step agents, structured outputs, and fine-tuning — as real product features with evaluation harnesses, guardrails, and cost engineering.
Unlike a ChatGPT wrapper, our LLM development services treat every model call as a decision with a cost, a latency budget, an accuracy target, and a failure mode. The work spans retrieval, prompt design, orchestration, tool use, safety, and integration with your existing systems — delivered as part of a broader AI development services relationship, not a standalone demo.
Every LLM development engagement ships with an evaluation harness — golden-set regression tests, LLM-as-judge scoring, and human review sampling in production. Accuracy targets are defined upfront and tracked continuously, so model regressions are caught before your users are.
Our LLM development team is US-based and senior. Credentials from Columbia, Harvard, Oxford, Apple, Citi, and Wells Fargo — engineers who ship LLM features into regulated production environments, not prompt engineers producing demos.
LLM development at NextGen is full-stack. We build the retrieval, the orchestration, the evaluation, the guardrails, and the integration into your product — so the LLM feature ships as software your users use, not a Jupyter notebook your data team maintains.
We standardize on OpenAI, Anthropic Claude, Azure OpenAI (BAA-covered), AWS Bedrock, Google Vertex, and open-source models including Llama 3, Mistral, and Qwen. Model tiering, prompt caching, and semantic caching typically cut inference cost 60–80% versus naive GPT-4 use.
Copilots, search, summarization, agents, and structured extraction wired into existing product surfaces.
Healthcare, insurance, tax, legal, and financial services where hallucination and PII handling aren't acceptable.
Document intelligence, RFP generation, ticket routing, and internal knowledge search built on top of LLMs.
You have a working demo. You need production reliability, evaluation, and cost control — that's LLM development services, not more prompting.
Your value lives in your data. Custom LLM development services with RAG over your corpus beats any off-the-shelf chatbot.
Founding technical teams that need senior LLM engineering capacity without hiring three full-time engineers in a year.
Vector search, hybrid retrieval with BM25 + embeddings, chunking strategies, re-ranking, and grounded answers with citations. RAG is how most enterprise LLM apps hit their accuracy target — not by fine-tuning.
Agents that call tools, hit APIs, chain reasoning steps, and return structured results. Function calling, typed tool schemas, retries, fallbacks, and human-in-the-loop review patterns for high-stakes actions.
Prompt design, few-shot examples, JSON-mode and schema-constrained outputs, and prompt versioning tied to evaluation baselines so improvements are measurable, not vibes.
Supervised fine-tuning and DPO on open-source models when RAG + prompting can't hit the target — for tone, format, or narrow-domain specialization.
Golden-set regression tests, LLM-as-judge scoring, and production human-review sampling. Every deployment ships with accuracy metrics you can watch move.
Model tiering, prompt caching, semantic caching, and routing to cheaper models where quality allows. Typical result: 60–80% inference cost reduction versus naive GPT-4 baselines.
Input/output guardrails, PII redaction, prompt-injection defenses, and content policy filters. Audit logging on every model call for regulated workflows.
Traces, prompt/response logging, cost tracking, and drift detection wired into your existing observability stack (Datadog, Grafana, Honeycomb).
Map use cases to value, identify data readiness, define guardrails and accuracy targets — before writing any prompts.
Working RAG or agent prototype against real data. Evaluation harness with baseline accuracy numbers and a go/no-go recommendation.
Ship to production with monitoring, cost tracking, retries, fallbacks, guardrails, and human review where the risk profile demands it.
Continuous evaluation, prompt and retrieval tuning, model upgrades, and cost engineering as usage scales.
4-week fixed-scope RAG or agent prototype with evaluation harness and go/no-go recommendation. Typical range: $40K–$90K.
Senior US-based LLM engineer embedded into your team. From $16K/month.
LLM engineer + full-stack + data engineering pod delivering LLM features end-to-end. $40K–$80K/month.
End-to-end LLM feature to production quality, typically $80K–$400K depending on data readiness and integration surface.
All pricing is transparent and US-market calibrated. We don't compete on the lowest upfront number — we compete on delivering outcomes that generate the highest return on investment.
Field extraction and structured-output pipelines hitting 95%+ accuracy with named evaluation metrics on a golden set.
Model tiering, caching, and prompt design have cut inference cost 60–80% versus naive GPT-4 baselines on live features.
First LLM features shipping to production in 8–12 weeks, not quarters — because engineering discipline is applied from day one.
LLM features shipping inside HIPAA and SOC 2 environments with BAA-covered inference, PII controls, and audit logging that satisfy compliance reviews.
95%+ field extraction accuracy across heterogeneous FOIA releases
LLM-driven document intelligence pipeline — layout-aware OCR, entity extraction, and evaluation harness scoring accuracy on a golden set. Same-day turnaround on a research task that previously took weeks.
Read the full case study70%+ call deflection with HIPAA-compliant LLM agents
Multi-step LLM agents that triage inbound patient calls, resolve routine requests end-to-end, and hand off escalations with full context. BAA-covered inference and clinical safety guardrails on every deployment.
Read the full case studyChunking, retrieval, re-ranking, and evaluation — the details that separate a demo from a production LLM feature.
Model tiering, prompt and semantic caching, and prompt design to cut inference cost without sacrificing accuracy.
Golden sets, LLM-as-judge, and human review — the evaluation stack that catches regressions before your users do.
For internal ad-hoc use, sure. For production features grounded in your data, wired into your product, and evaluated against your accuracy targets, you need custom LLM development — not a shared chat window.
Usually no. RAG plus prompt engineering hits the accuracy target for most use cases and is faster, cheaper, and easier to update. Fine-tuning is reserved for tone, format, or narrow-domain specialization.
Retrieval grounding, structured outputs with schema validation, evaluation harnesses that catch regressions, guardrails, and human-in-the-loop review for high-stakes decisions. Hallucination isn't eliminated — it's engineered around.
We build model-agnostic where feasible — swappable model clients, prompt versioning, and evaluation harnesses that let you compare providers empirically. Lock-in only shows up when specific features (structured outputs, function calling, extended context) matter.
LLM development services are the engineering practice of building production applications on top of large language models — retrieval-augmented generation, agents, function calling, structured outputs, fine-tuning, and evaluation. NextGen delivers LLM development services as part of a broader AI development company engagement, not a standalone consultancy.
Dedicated LLM engineers start at $16K/month. AI pods run $40K–$80K/month. 4-week prototype sprints run $40K–$90K. Full production LLM features typically land at $80K–$400K depending on data readiness and integration surface. See our AI development cost guide for a full breakdown.
OpenAI (GPT-4, GPT-4o, o-series), Anthropic Claude (Sonnet, Opus, Haiku), Azure OpenAI with BAA coverage, AWS Bedrock, Google Vertex (Gemini), and open-source models including Llama 3, Mistral, Qwen, and DeepSeek.
Yes — supervised fine-tuning and DPO on open-source models when RAG plus prompting can't hit the accuracy target. We default to RAG first because it's faster, cheaper, and easier to update.
Yes. Azure OpenAI, AWS Bedrock, and Google Vertex all support BAA-covered inference. We build with audited logging, PII redaction, and configurable retention — and can deploy on-prem inference where compliance requires it.
AI development is the broader category — including LLMs, computer vision, and classical ML. LLM development is the LLM-specific subset. Most modern AI development engagements are LLM-heavy but often include classical ML for classification and ranking.
NextGen builds AI systems that survive contact with production traffic. Our team combines applied ML research with the systems engineering required to run models at real-world reliability targets, and every AI engagement is scoped to a measurable business outcome rather than a demo.
All model development and MLOps work is performed by US-based engineers. Our proximity to US-hours stakeholders, familiarity with US privacy and AI regulatory posture (state AI acts, HIPAA, GLBA), and native English data curation produces AI systems that behave predictably in domestic production traffic. We serve clients nationwide from our NYC base.
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