A US-based AI development company shipping custom AI, AI/ML, and AI application development — LLM, RAG, OCR, and generative AI in production, not demos.
AI development services cover model selection, RAG pipelines, fine-tuning, agents, and the production plumbing around them. NextGen is a US-based AI development company: most AI builds run $80K-$400K and reach production in 8-20 weeks, with evaluation harnesses and cost controls built in from day one.
NextGen Coding Company staffs engagements exclusively with senior US-based engineers — no offshore subcontracting, no junior bench — with credentials from Columbia, Harvard, Oxford, Apple, Citi, and Wells Fargo. Engagements run $16,000–$28,000 per engineer per month on a dedicated-team model, and every one starts with a free 30-minute scoping call with an engineer rather than a salesperson.
Large financial datasets are handled by keeping computation in the warehouse and using the model only for interpretation. The LLM writes and explains queries; it never ingests millions of rows into a context window, which is both prohibitively expensive and unreliable for arithmetic. Deterministic calculation, model-driven explanation. NextGen builds this split so every number shown traces back to a query you can inspect.
A focused production AI system runs $80,000–$250,000; enterprise deployments with compliance review, integrations, and MLOps run $250,000–$1M+. The cost driver is rarely the model — it is data access, evaluation infrastructure, and integration with systems that were never designed for it. NextGen scopes with a discovery sprint that produces a fixed estimate you can budget against.
8–12 weeks for a scoped production system with evaluation and monitoring in place; 3–5 months when the work spans multiple integrations or regulated review. Any vendor quoting two weeks is describing a demo, and demos are exactly where most enterprise AI budgets die. NextGen's engagements are measured by what is running in production, not by what was shown in a pilot.
NextGen Coding Company builds production AI systems — LLM workflows, retrieval-augmented generation, document intelligence, and computer vision — grounded in real business outcomes. Our engineers ship AI features that hold up under real user traffic, real evaluation, and real accuracy requirements.
AI is not a demo problem. Every model call has a cost, an accuracy target, and a failure mode. NextGen builds systems with evaluation harnesses, guardrails, cost tracking, and human-in-the-loop review — the difference between AI that ships and AI that stalls.
We build AI for regulated and high-accuracy domains — insurance, tax, healthcare, and financial services — where hallucination or PII leakage isn't acceptable.
Our engineers are full-stack first, AI-specialized second. That means the AI ships as a real product feature, integrated end-to-end, not stranded in a Jupyter notebook.
Credentials from Columbia, Harvard, Oxford, Apple, Citi, and Wells Fargo. Deep experience with OpenAI, Anthropic, Bedrock, Vertex, and open-source models.
Adding AI features to existing products — search, summarization, agents, and copilots.
Automating document extraction, claims triage, and adjudication support.
AI for document classification, extraction, and audit assist.
Contract review, clause extraction, and precedent search.
Clinical documentation, prior authorization, and patient-facing copilots with HIPAA discipline.
Internal knowledge search, RFP generation, and ticket routing.
End-to-end machine learning development — model selection, fine-tuning, evaluation, and MLOps — for classification, forecasting, ranking, and recommendation workloads that run reliably in production.
Full AI application development — front-end, back-end, auth, billing, observability — wrapping LLM and ML features into real SaaS products your customers actually use, not internal prototypes.
Custom AI solutions built to your data, your workflows, and your compliance posture. Bespoke agents, copilots, and domain-specific models — no off-the-shelf template, no vendor lock-in.
Multi-step agents, tool use, function calling, and structured outputs wired into product workflows with retries, fallbacks, and human review.
Vector search, hybrid retrieval, chunking, re-ranking, and grounded answers with citations.
PDFs, contracts, invoices, claims, forms. Layout-aware parsing with structured outputs.
Accuracy metrics, regression tests, and human-review dashboards on every deployment.
PII handling, audit trails, policy filters, and safe-completion patterns for regulated workflows.
Map use cases to value, identify data readiness, define guardrails and evaluation criteria.
Build a working prototype against real data. Set up evaluation harness and accuracy baselines.
Ship to production with monitoring, cost tracking, retries, fallbacks, and human review where needed.
Continuous evaluation, prompt and retrieval tuning, model upgrades, and ongoing capacity.
Dedicated US-based AI engineer, from $16K/mo, embedded into your team.
AI + full-stack + data engineering pod delivering AI features end-to-end.
4-week fixed-scope prototype with evaluation harness and go/no-go recommendation.
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.
Document extraction pipelines hitting 95%+ field accuracy with structured evaluation.
Model selection and prompt tuning have cut inference cost 60–80% versus naive GPT-4 use.
AI features shipping to production in weeks, not quarters — because engineering discipline is applied from day one.
AI in insurance, tax, and healthcare workflows with audit trails and PII controls that satisfy compliance.
70%+ call deflection with HIPAA-compliant AI agents
NextGen built an agentic AI call center that triages inbound patient calls, resolves routine requests end-to-end, and hands off escalations with full context. Evaluation harness tracks intent-recognition accuracy and clinical safety guardrails on every deployment.
Read the full case study10x faster HIPAA risk assessments with audit-grade accuracy
Custom AI development for a HIPAA compliance platform that ingests policies, technical controls, and workflow evidence and produces auditor-ready risk assessments. Shipped with structured outputs, PII handling, and human-in-the-loop review on every finding.
Read the full case study95%+ field extraction accuracy across heterogeneous FOIA releases
Document intelligence pipeline for FOIA responses — layout-aware OCR, LLM-driven entity extraction, and evaluation harness scoring extraction accuracy on a golden set. Turned a manual multi-week research task into a same-day workflow.
Read the full case studyMulti-language classification pipeline shipped with real-time evaluation
Generative AI development for a threat-intelligence platform combining translation, entity extraction, and classification with strict provenance and audit logging. Model tiering cut inference cost by more than 60% versus a naive GPT-4 baseline.
Read the full case studyChunking, retrieval, re-ranking, and evaluation — the details that separate demos from production.
Model tiering, caching, and prompt design to cut inference cost without sacrificing quality.
Guardrails, audit trails, and human review patterns for insurance, healthcare, and financial services.
For internal ad-hoc use, sure. For production features with accuracy requirements, cost control, and data privacy, you need engineered AI — not a chat window.
Every engagement includes an evaluation harness. Accuracy targets are defined upfront and measured continuously — we don't ship what can't be evaluated.
We support Azure OpenAI, Bedrock, on-prem models, and private inference. PII handling and data residency are addressed in the readiness assessment.
AI augments teams — routing, drafting, extracting — while humans handle exceptions and judgment. That's how it actually ships in regulated domains.
AI development is the engineering discipline of designing, building, evaluating, and operating software features powered by machine learning models — including large language models (LLMs), computer vision, and predictive ML. It spans data pipelines, model selection or fine-tuning, prompt and retrieval design, guardrails, evaluation harnesses, and production integration into real applications.
Most production AI development engagements land between $80K and $400K for the first shipped feature, depending on data readiness, accuracy targets, and integration surface. NextGen offers dedicated AI engineers from $16K/month, AI + full-stack pods, and 4-week fixed-scope prototype sprints so you can right-size the investment.
A production-ready AI feature typically takes 8–16 weeks: 2–4 weeks for readiness assessment and prototype, 4–8 weeks for the production build with evaluation harness and guardrails, and an ongoing operate-and-improve phase. Fine-tuning, regulated data, or heavy legacy integrations extend the timeline.
Machine learning development is a subset of AI development focused on training and deploying statistical models — classification, regression, forecasting, ranking. AI development is broader: it also covers LLM workflows, retrieval-augmented generation, agents, document intelligence, and computer vision. Most modern AI products combine both.
A custom AI solution is an AI system built specifically for your data, workflows, users, and compliance requirements — as opposed to a generic SaaS chatbot or off-the-shelf tool. Custom AI development typically includes bespoke retrieval, domain-tuned prompts or fine-tuned models, private inference, and deep integration with your existing systems.
Generative AI development is the practice of building applications on top of models that produce new content — text, code, images, audio, or structured data. It covers LLM apps, RAG systems, copilots, agents, and multimodal features, along with the evaluation, cost engineering, and safety controls needed to ship them to production.
OpenAI, Anthropic Claude, Azure OpenAI, AWS Bedrock, Google Vertex, and open-source models including Llama and Mistral — deployed on your cloud with BAA-covered inference and audited logging where compliance requires it.
Every 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 users are.
Yes. Our LLM development services cover prompt engineering, retrieval-augmented generation, function calling, agents, and fine-tuning. Generative AI development covers text, code, image, and multimodal features. Both are delivered as part of a broader AI development company engagement with production discipline.
Yes — we build AI features that plug into Salesforce, NetSuite, HubSpot, custom platforms, and legacy systems. Integration work is scoped in the readiness assessment and delivered by the same engineers who build the AI, so nothing gets thrown over a wall.
An AI development company designs, builds, and operates AI systems as production software — not a consultancy that hands you a slide deck. NextGen is a US-based AI development company staffed with senior engineers from Columbia, Harvard, Oxford, Apple, Citi, and Wells Fargo, focused on regulated and high-accuracy domains where hallucinations aren't acceptable.
NextGen Coding Company is a US-based AI development company building production AI systems for regulated and high-accuracy domains. Team credentials from Columbia, Harvard, Oxford, Apple, Citi, and Wells Fargo.
Our AI engineering team is entirely US-based, serving clients nationwide. Data residency, compliance conversations, and production support all benefit from a team in your time zone with real accountability.
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