Generative AI Development Services that Ship to Production
Embedded nearshore AI engineers, ML practitioners, and agentic systems architects who turn proofs of concept into production-grade software. Next starts here.
Trusted by engineering teams in Financial Services, Healthcare, Media & Publishing, Hi-Tech, Energy & Utilities, and Non-Profit organizations.
Trusted by
When AI Pilots Stall,
Roadmaps Burn Cash
Most AI initiatives don’t fail at the model. They fail at the handoff. The pilot demos beautifully, and six months later it’s still in a notebook; never wired into the data pipeline, the auth layer, or the audit trail.
Our generative AI development services start where most agencies stop. We embed senior AI engineers and ML practitioners to ship the actual system, whether that’s AI automation services for a single workflow or a multi-quarter agentic AI buildout.
What We Offer
in AI
Four service lines, designed to plug in at any stage of your AI maturity; from first proof of concept to production-grade agentic systems.
Generative AI Development Services
End-to-end build of LLM-powered features inside your existing product: retrieval-augmented generation, semantic search, AI copilots, and document intelligence. We own the full stack: data prep, model selection, prompt engineering, evaluation pipelines, and the production monitoring most teams forget about until it breaks.
AI Automation Services
Workflow automation powered by AI agents and intelligent pipelines. Document processing, customer service automation, contract analysis, internal operations. We identify the workflows where AI returns real ROI, not the ones that just look impressive in demos.
Agentic AI & Multi-Agent Systems
Multi-agent AI systems and agentic platform integrations for organizations moving beyond single-prompt LLM calls. Tool use, orchestration, state management, and guardrails; built to work inside your existing security and compliance perimeter.
AI Proof of Concept to Production
A focused engagement that takes a single AI use case from idea to production in a defined scope. Includes feasibility analysis, model selection, prototype, evaluation framework, and a go/no-go gate before scaling. For teams that need to validate before committing a budget.
How Our Generative AI Development
Services Work
Step 01
Diagnose
An embedded discovery. We sit with your engineering and product teams, map the use case against your data, infrastructure, and compliance constraints, and deliver a feasibility brief, not a strategy deck. You leave knowing what’s possible, what it will cost, and where the risks live.
Step 02
Build
Senior AI engineers, ML practitioners, and data engineers embed alongside your team under one of our four engagement models: Outcomes, Teams, Capacity, or Managed Services. You choose the shape; we bring the depth.
Step 03
Operate
We hand off systems that survive in production: evaluation harnesses, cost monitoring, drift detection, and human-in-the-loop workflows. Most engagements graduate into long-term partnerships.
Most engagements graduate into long-term partnerships.
Industries We Serve
AI in a regulated bank looks nothing like AI in a streaming product.
Our AI automation services and generative AI development services adapt to the constraints of your industry: compliance, data residency, model governance, and the business cases that actually move your numbers.
Financial Services
Audit-ready AI for banks, fintech, and insurance. Fraud detection, document intelligence, agentic compliance workflows; built inside SOC 2 and PCI-DSS perimeters.
Healthcare
HIPAA-aligned AI systems for patient-facing tools, clinical decision support, and provider operations. Models that respect PHI and clinical workflows.
Media & Publishing
Content intelligence, automated metadata, semantic search across archives, and AI-assisted editorial workflows. Built for the cadence of newsrooms and streaming releases.
Hi-Tech
Embedded AI features for SaaS and platform products. Copilots, RAG over product docs, smart automations; and the evaluation infrastructure to keep them honest at scale.
Energy & Utilities
AI for asset-heavy, ESG-driven environments. Predictive maintenance, grid optimization, and operational intelligence; where downtime is measured in millions.
Non-Profit
AI that justifies every dollar. Donor intelligence, program impact analytics, and operational automation sized to mission, not headcount.
Four Ways to Work
with Our AI Teams
You don’t buy AI by the hour. You buy the shape of delivery that fits where your organization actually is right now.
Outcomes
We own a defined business outcome end-to-end discovery, build, and release.
Teams
A self-managed nearshore agile pod, embedded inside your engineering org. We bring the team; you bring the product context.
Capacity
Senior engineers and agile coaches added to your existing teams. You manage; we strengthen.
Best for: filling specific skill gaps or accelerating a roadmap where the structure already works.
Managed Services
We run the operation end-to-end delivery, support, and continuous improvement; under defined SLAs.
Best for: ongoing product or platform work where you want predictable outcomes without managing the day-to-day.
Why Financial Institutions Stay With Us
The Nearshore Advantage
That Delivers Results
We combine the cost benefits of offshore with the collaboration ease of onshore.
Our teams across the Americas corridor work in your timezone, speak your language, and understand your culture.
Timezone Collaboration
Work with teams who share your working hours. No late-night calls or delayed responses.
Rapid Team Assembly
Get your dedicated team up and running in 2–4 weeks, not months. We match talent to your specific needs.
Enterprise Quality
Our engineers are rigorously vetted and trained in best practices used by Fortune 500 companies.
Scalable Solutions
Start small and scale up seamlessly. Our flexible engagement models adapt to your changing needs.
Cultural Alignment
Strong cultural affinity with US business practices ensures smooth communication and collaboration.
24/7 Operations
Your success is our priority. Get dedicated account management and 24/7 operations when you need it.
Verified Reviews
Average Review Rating
Recognition
Inc. 5000
Fastest Growing Companies
10 time recipient
Portland's Fastest Growing
Portland Business Journal
10 years in a row
Clutch
Top 5 Nearshore Software Developers in Mexico
2025
Partners
AWS Partner Network
Amazon Web Services
Databricks Partner
Data + AI Platform
Ready to Ship
AI to Production?
Book a 30-minute technical consultation with one of our senior AI engineers. No deck, no sales pitch, we’ll review one specific use case you’re stuck on and tell you what we’d do about it.
What are generative AI development services?
Generative AI development services are advisory and embedded engagements that take generative AI use cases from idea to production. At unosquare, this includes feasibility analysis, model selection, RAG pipelines, agentic workflow design, evaluation harnesses, and the production engineering: observability, cost monitoring, drift detection; that keeps the system honest after launch. The goal is shipped software, not slide decks.
How are AI automation services different from generative AI development?
AI automation services focus on workflows: document processing, customer service routing, internal operations, contract analysis. Generative AI development services typically involve building a new product feature or capability: a copilot, a RAG search experience, an agentic system. Most clients need both, sequenced correctly: automate the workflows that have clear ROI, then invest in generative features once the data foundation is solid.
Should we start with an AI proof of concept or jump straight to production?
It depends on how well-defined the use case is. If you can describe the input, the output, and how you’d measure success in one sentence, you can often skip the PoC and go straight to a production-bound build with a go/no-go gate early in the engagement. If the use case is exploratory (‘we think AI could help here, but we’re not sure how’), a focused proof of concept is the lower-risk path. Either way, the PoC should be built on production-quality data and infrastructure, not a sandbox that won’t translate.
Why nearshore AI teams instead of onshore or offshore?
Time zones decide whether AI engagements actually work. Offshore teams break the daily feedback loop that AI development requires: model evaluation, prompt iteration, and integration testing all need real-time conversations. Onshore teams cost three to four times more for the same depth. Our nearshore AI teams operate in U.S.-aligned time zones, which means real-time pairing, same-day decisions, and a true co-delivery rhythm with your engineering org.
Do you build with OpenAI, Anthropic, open-source models, or all of the above?
All of the above. We’re model-agnostic. The right choice depends on cost, latency, data residency, fine-tuning needs, and what your security review will actually approve. Most production systems we build use a mix: a frontier model for complex reasoning, a smaller fine-tuned model for high-volume tasks, and open-source models when data sensitivity makes API calls a non-starter. We make the recommendation in the feasibility phase, not in the sales call.
How do you handle data privacy, compliance, and model governance?
Compliance is baked in from week one. We work inside SOC 2, HIPAA, PCI-DSS, and financial audit perimeters regularly. That means data residency controls, prompt and response logging, PII redaction pipelines, model card documentation, evaluation harnesses for bias and hallucination, and human-in-the-loop checkpoints for high-stakes outputs. If your security team needs to approve every step, we structure the engagement around that, not despite it.
Which engagement model should we choose: Outcomes, Teams, Capacity, or Managed Services?
Pick Outcomes if you have a specific business result that needs to be delivered and you’d rather hand it off completely.
Pick Teams if you need to scale delivery capacity without adding internal management. Pick Capacity if your structure works and you just need senior engineers plugged into existing teams.
Pick Managed Services if you want us to run the operation end-to-end under defined SLAs, so you can focus on the business instead of the day-to-day.
Most clients start with one model and graduate into another as the relationship deepens.
Next Starts Here.
Talk to an unosquare AI specialist. We'll diagnose one friction point in your current delivery and tell you exactly what we'd do about it.