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Your AI roadmap is approved. Your team needs the engineers to ship it.
Why most AI roadmaps stall
The board approved the AI budget. The roadmap is committed. The team can't execute it alone.
Sancrisoft provides senior nearshore AI engineers for US product and engineering teams. Our LATAM-based team integrates LLMs, builds agentic workflows, and ships production-grade AI features with US timezone overlap, full technical ownership, and a structured methodology that gates every release behind human sign-off.
You know what good AI architecture looks like. The problem is not the vision; it is the execution gap between what the roadmap promises and what your current team can ship this quarter.
The AI talent market will not wait for your timeline
Senior AI engineers in the US take 6+ months to hire, with salaries ranging from $ 180K to $400K+ per year. By the time your request closes, the window has moved.
A convincing demo is not a production system
No eval plan, no guardrails, no architecture. The agentic workflow collapses the moment it leaves the slide deck and hits real user traffic.
Knowing software does not mean knowing production AI
LLM routing, agent orchestration, evals, and observability are a separate discipline. Most engineering teams have not shipped this yet.
Is this your engineering reality?
Not every product team is the right fit for how we work. These are the ones who usually are:
You have board-level AI commitments and need to ship features this quarter.
Your team knows software, not production LLMs or agentic workflows.
You tried an AI vendor and got a demo that never reached production.
You need senior AI engineers embedded in your team without a 6-month hiring cycle.
You need a nearshore AI partner with a verifiable production track record.
You need honest guidance on where AI fits your architecture and where it does not.
The real cost
Every quarter the AI roadmap slips, a competitor ships it first. A vendor that demos but cannot reach production does not just cost time, it costs the credibility of the entire AI initiative with your board.
Technical ownership, not ticket execution
Our engineers do not wait for specs. They review your architecture, identify risks before they become blockers, and own the technical decisions alongside your team, the way a senior AI hire would. That means fewer escalations, faster unblocking, and no context switching on your end. You spend your time reviewing decisions, not re-explaining requirements to engineers who are waiting to be told what to do.
Production AI requires more than a model call
LLM routing, guardrails, eval-gated releases, and observability are not optional layers; they are what separate AI that scales from AI that fails in production. We build all of it in from the start. Most production AI incidents trace back to a missing eval gate or an unmonitored edge case. The teams that learn this the hard way spend more time rebuilding than shipping. We design around that failure mode before it happens.
Verifiable AI track record
Venice AI, a privacy-first LLM platform handling real user traffic at scale, was built by this team. We can walk you through the architecture, the eval plan, and the production decisions, not a deck, the actual system. That transparency is deliberate. Most vendors protect their processes because they do not hold up to scrutiny. We show ours because it does. If you can evaluate the architecture, you can evaluate the team.
CONDUCT: A structured pipeline where agents build at velocity and engineers own every decision
01
Plan first. Build with certainty
Architecture, model selection and routing, data flow, and an eval plan, defined and approved before a line of code. You sign off on scope, price, and timeline before we build anything.
02
Velocity without the shortcuts
Specialized AI agents are implemented at velocity. Senior engineers own the architecture, direct every technical decision, and review every output. Velocity without shortcuts.
03
Test & Verify
Every release undergoes automated testing and a mandatory human review gate before being deployed to production. Guardrails and evals gate each release. Nothing ships unverified.
Why Conduct works
Any team can call a model. Fewer can build the system around it, the routing, guardrails, evals, and accountability that make production AI reliable. That system is what Conduct delivers.
Work
Production AI. Shipped. Verifiable
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Is your AI roadmap waiting on the right team?
Book a 30-minute call with our senior AI team. We will review your stack, your current architecture, and give you a clear technical path on what it takes, what it costs, and when it ships.
Frequently Asked Questions
Yes. Sancrisoft shows real nearshore AI development shipped to production, not a portfolio page. We walk you through the architecture, model selection decisions, eval plan, and production constraints of live AI systems handling real user traffic at scale. For engagements under NDA, we provide a technical walkthrough in a recorded call if necessary. We do not rely on a case study PDF. We show you the actual system and answer technical questions directly.
Sancrisoft designs guardrails, evals, and production reliability into nearshore AI features from the Spec phase before a line of code. Every AI feature is gated by automated evaluations running against defined performance and safety thresholds before deployment. Post-launch, we set up observability tooling so your team can monitor model performance, latency, and cost in real time. Eval-gated releases are non-negotiable in Conduct, not an optional layer added if the client asks. Production reliability is built in from the architecture, not retrofitted after launch.
Sancrisoft treats data privacy and compliance in nearshore AI development as architecture constraints, not afterthoughts. In the Spec phase, we review your specific requirements for HIPAA for healthcare products and design the AI integration accordingly. We work with OpenAI and Anthropic enterprise tiers, both offering data processing agreements and zero-data-retention options. We do not send regulated data to models that are not covered. Compliance posture is defined before we write a line of code.
Sancrisoft's nearshore AI development team works with the full production AI stack. LLM providers include OpenAI, Anthropic Claude, Meta Llama, and Google Gemini. For orchestration and agentic systems, we use LangChain, LangGraph, and Model Context Protocol (MCP). We recommend tools based on your architecture and constraints, not our preferences. If your stack is already partially defined, we integrate with what you have rather than replacing it.