Most companies that say they can “build you an AI product” have never taken one past a demo. TechEniac is different. When you hire AI developers from our team, you’re hiring engineers who have shipped LLM applications, multi-agent systems, and RAG pipelines into real production environments with the monitoring, evaluation, and scaling work that goes with it.
Whether you’re a startup founder validating an AI-first product or a CTO filling a capability gap without a six-month hiring cycle, our AI-first approach combines strategy, engineering, and data-driven development to deliver scalable, secure, high-performing AI products.
Time to start: 8–12 weeks average
Vetting for production AI experience: manual, inconsistent
Cost structure: salary + benefits + tooling + retention risk
Scaling up or down: requires new hiring cycles
Access to multi-agent, RAG, and LLM specialists: rare, hard to source
Time to start: 1–2 weeks
Vetting for production AI experience: pre-vetted for shipped AI systems
Cost structure: predictable, engagement-based pricing
Scaling up or down: flexible team resizing
Access to multi-agent, RAG, and LLM specialists: core specialization
End-to-end build of AI-native products architecture, data pipelines, model integration, and the application layer around them.
Autonomous and semi-autonomous agents that execute multi-step tasks, call tools, and operate reliably within defined guardrails.
Coordinated agent architectures for complex workflows where multiple specialized agents need to plan, delegate, and verify each other's work.
Fine-tuning, prompt architecture, evaluation pipelines, and integration of large language models into existing software.
Retrieval-augmented generation pipelines built for accuracy, source grounding, and low hallucination rates not just a vector database bolted onto a chatbot.
Full-stack AI SaaS products, from MVP to enterprise-grade release, including billing, multi-tenancy, and usage-based infrastructure.
Workflow automation that replaces manual, repetitive business processes using AI decision-making rather than static rule engines.
Conversational systems connected to your CRM, support stack, or internal data built for accuracy and escalation handling, not just Q&A.
Embedding AI capability into your existing platforms ERP, CRM, internal tools without disrupting what already works.
Governed, secure, and auditable AI systems built to enterprise compliance and reliability standards.
Best for
Ongoing product development, single specialist need
Structure
One developer embedded in your team, full-time
Best for
Full product builds, multiple parallel workstreams
Structure
Cross-functional team (AI engineers, backend, product)
Best for
Defined scope, fixed timeline, fixed deliverables
Structure
Scoped SOW with milestones
Best for
Short-term needs, audits, advisory, or overflow work
Structure
Flexible hours, no long-term commitment
We define the technical problem, not just the job title. What are you actually trying to build or fix?
We match developers based on relevant AI system experience, not generic AI/ML keyword matching.
You meet the developer or team lead directly before commitment.
Access, tooling, and workflow integration handled within days, not weeks.
Regular technical check-ins, sprint reporting, and direct communication with engineers not account managers relaying messages.
We build production AI systems, not chatbot demos wrapped around an API call.
Our engineers understand the evaluation, monitoring, and failure modes of AI systems not just how to call a model.
We work primarily with US startups and enterprises, so we understand US compliance, security, and delivery expectations.
Architecture decisions are made for maintainability and scale, not just to hit a demo deadline.
Everything you need to know before getting started.
