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Services

Generative AI Development Company

Production-grade generative AI for enterprises that are done with proofs-of-concept.

AlphaCorp AI is a generative AI development company that designs, builds, and operates LLM-powered systems: AI agents, retrieval-augmented generation (RAG) pipelines, fine-tuned models, and the software around them. We build on foundation models from Anthropic, OpenAI, Google, and the open-weight ecosystem instead of training models from scratch, so your budget goes to engineering and evaluation rather than nine-figure compute. Around 70% of organizations used generative AI in at least one business function in 2025, yet most deployments never survive contact with production traffic. Closing that gap is the entire job.

Track record

Creators of RustyRAG

Realtime RAG, built in Rust · Sub-200ms end-to-end

10+ years delivering AI solutionsIgnas Vaitukaitis · Founder & CEO

Read RustyRAG’s source before you sign.

Shipped for

VersarWashington, DC
GynisusNew York
CampusReelNew York
LuniqGermany
HospitalityFlowSingapore

Generative AI development in 2026: the numbers that frame the decision

The 2026 numbers describe a wide gap between experimentation and real adoption, and that gap is where a development partner earns its fee. Casual use is everywhere. Committed adoption is still rare.

20.2%of OECD firms formally reported using AI in 2025, up from 8.7% in 2023OECD, 2025 ↗

22–94%hallucination-rate range across 26 top models in a 2026 accuracy benchmarkStanford HAI, 2026 ↗

83%of all-time Hugging Face downloads go to models under 1B parametersHugging Face, 2026 ↗

Overview

What our generative AI development company builds

AlphaCorp AI builds six things, and each one maps to a dedicated engineering practice rather than a generic "AI solutions" bucket.

AI agent development

Agents that plan, call tools, and complete tasks, wired to your systems through the Model Context Protocol, the open standard Anthropic introduced in November 2024, instead of brittle one-off integrations per data source.

RAG pipeline development

Retrieval systems that ground model outputs in your own data without retraining, built on RustyRAG and designed against the structural failure modes documented in 2025 and 2026 RAG research.

Model fine-tuning

Parameter-efficient methods (LoRA, adapters, prefix-tuning) for domain adaptation when prompting and retrieval alone stop improving accuracy.

Prompt engineering and evaluation

Versioned prompts with regression suites, so a model upgrade or vendor price cut is a routing change, never a rewrite.

LLMOps infrastructure

Caching, model routing, token-budget controls, and monitoring. A single LLM call can cost orders of magnitude more than a traditional ML prediction, so cost engineering starts on day one.

AI-integrated software engineering

The interfaces, APIs, and data plumbing that turn a working model into a working product.

03Stack

The Stack We Ship On

We pick the best tool for each job, not the trendiest. This is what runs behind the agents, retrieval pipelines and automation we put into production.

Languages

PythonRustTypeScript

Foundation Models

AnthropicOpenAIGeminiLlamaMistralHugging Face

Fast Inference

GroqCerebrasOpenRouterReplicateOllamavLLM

Agents & Orchestration

LangGraphLangChainLlamaIndexCrewAIn8n

Vector & Memory

MilvusPineconepgvectorChromaWeaviateRedis

Voice, Image & Fine-Tuning

ElevenLabsLiveKitVapiComfyUIPyTorch / LoRAModal

Cloud & Delivery

AWSAzureGoogle CloudDockerKubernetesVercel

Evals & Observability

LangSmithLangfuseWeights & BiasesGrafana

Process

How an AlphaCorp AI generative AI engagement runs

An AlphaCorp AI engagement runs in five stages, each with a concrete artifact you keep.

Scoping and integration audit

We map the workflow, the data, and the cost of a wrong answer, then tell you plainly whether generative AI fits. Sometimes the answer is no.

Architecture and model selection

Closed API or open-weight, per component. In practice that usually means routing across model tiers rather than paying frontier prices for every call.

Build with evaluation wired in

Every prompt and retrieval change runs against a test suite before it ships. One detail we learned the hard way: accuracy collapses when a false claim is framed as the user's own belief, so our conversational test sets include exactly that framing.

Hardening and deployment

Caching, fallbacks, token budgets, access controls, and a security review before production traffic.

Operation and measurement

Live monitoring of accuracy, latency, and cost per outcome, with incident tracking instead of vibes.

Benefits

Why hire a generative AI development company in 2026

Hiring a generative AI development company in 2026 solves a staffing problem and a moving-target problem at the same time.

Expertise is the documented bottleneck

When 70.9% of EU non-adopters cite missing skills as their barrier, per the OECD's December 2025 report on SME adoption, an external build team is the shortest path around a multi-quarter hiring cycle. The BLS projects software-developer employment growing 15.8% from 2024 to 2034, so that hiring market only tightens.

Building on models beats building models

Stanford's 2026 AI Index puts GPT-4-class training at roughly $78 to $100 million and Meta's Llama 3.1 405B near $170 million in compute. Nobody sane replicates that. The value sits in the application layer, which is exactly what we build.

Per-token economics shift monthly

OpenAI cut standard API pricing 20 to 33% in August 2026 alone. Architectures with model routing capture those cuts automatically. Hardcoded ones do not.

Production favors small models

Among Hugging Face models with declared sizes, those under 1 billion parameters account for the large majority of all-time downloads, while 100B+ models account for about 1%. Real deployments run lean, and building lean is a craft.

Why AlphaCorp AI

The generative AI development company behind RustyRAG

AlphaCorp AI built RustyRAG, our open-source retrieval engine, tuned to answer in under 200 milliseconds. The engineers who maintain it are the same people staffed on client work. Founded by Ignas Vaitukaitis, the remote-first team works on US Eastern hours in English, Portuguese, and Spanish, for enterprises in healthcare, financial services, SaaS, and logistics.

Teams choose AlphaCorp AI because we measure before we promise, and we say so in writing.

We will tell you when AI slows you down. A 2025 randomized controlled trial found experienced developers took 19% longer with AI coding tools on mature, unfamiliar codebases, even while believing the tools had saved them 20%. Any vendor quoting a universal productivity number is selling perception. We instrument the workflow and report what the data says.

The people you talk to are the people who build. No handoff from sales engineers to an offshore bench. Your architecture calls happen with the engineers writing the code.

Token economics are treated as engineering. Caching, routing, and tiering get designed before launch, because inference is the recurring bill that decides whether the system survives its second year.

One boundary, stated plainly. AlphaCorp AI does not train foundation models from scratch, and we will not pretend otherwise. If you need a frontier model built, you need a lab with 25,000 GPUs. That honesty costs us some deals. We keep it anyway.

How AlphaCorp AI handles security and compliance in generative AI builds

AlphaCorp AI builds generative AI systems against published standards instead of self-invented checklists. Our reference frame is NIST's Generative AI Profile, NIST-AI-600-1, released July 26, 2024, which names twelve risk categories including confabulation, data privacy, information security, and value-chain integration.

For EU-exposed clients, we design documentation trails compatible with the AI Act's general-purpose AI obligations, in force since August 2, 2025, with enforcement possible from August 2, 2026. We also apply the data-supply-chain and data-drift controls from the joint CISA, NSA, and FBI AI data security guidance of May 2025. We describe practices rather than wave certifications: access is scoped per engagement, evaluation data is versioned, and where your data lives is decided with your security team before a single token is processed.

Bring the workflow that is costing you the most. In one working session, our engineers will tell you whether generative AI fixes it, roughly what that takes, and where we would start.

FAQ

Generative AI development company FAQs

What does a generative AI development company actually do?

A generative AI development company builds working software on top of existing foundation models: agents, RAG pipelines, fine-tuned models, and the evaluation and operations layer around them. AlphaCorp AI covers that full lifecycle, from data curation and retrieval design through deployment and monitoring, which academic lifecycle surveys treat as one connected stack rather than a model plus glue code.

How much does generative AI development cost?

Scope decides it, and a scoping session prices it. There is no honest flat rate for custom work. What we can date precisely: as of August 2026, frontier API inference runs about $5 per million input tokens and $30 per million output tokens at OpenAI's top tier, with far cheaper mid-tier options, which is why our architectures route most traffic to smaller models.

How long does a generative AI project take?

Timeline follows integration surface and evaluation depth, and the scoping stage produces a dated schedule before you commit to a build. A single retrieval assistant on clean data moves fast. An agent touching five internal systems with regulatory exposure does not, and we will not pretend it will.

Should we build in-house instead of hiring a generative AI development company?

Build in-house if you already have engineers who have shipped LLM systems to production; hire AlphaCorp AI if you do not, or if you need the first system live while your team levels up. The OECD's 2025 data shows missing expertise is the single most-cited adoption barrier, and the 2025 developer-productivity trial shows AI tooling alone does not substitute for that expertise.

Will AlphaCorp AI use open-weight or closed API models?

Both, chosen per component. Open-weight models grew from roughly a third of OpenRouter's served tokens in late 2025 to a majority by mid-2026, so dismissing them outright leaves money on the table, while closed frontier APIs still win on hard reasoning tasks. We benchmark on your data and route accordingly.

What happens after the generative AI system launches?

After launch, AlphaCorp AI runs the LLMOps loop: monitoring accuracy, latency, and cost, versioning prompts, and regression-testing every model swap. Documented AI incidents rose to 362 in 2025 from 233 in 2024, per the AI Incident Database figures in Stanford's 2026 AI Index. Unwatched systems drift. Watched ones compound.

Updated August 31, 2026