From Prototype to Your First Enterprise Logo

Talentica works in four stages:

• Design (1 to 2 weeks): We plan how people will use the AI. That includes where a person checks and approves its work, and what happens when it gives a different answer each time.
• Build (3 to 6 weeks): We get your data in order first. Then we pick the right AI model and tune it on your real data.
• Harden (2 to 4 weeks): We get it ready for big customers’ scrutiny. That means deployment, testing, safety guardrails and monitoring.
• Scale (ongoing): As more people use it, we keep it fast and keep costs under control.

Usually 6 to 12 weeks. That covers Design, Build and Harden. Scale work carries on after that. The exact time depends mostly on how ready your data is and how strict your enterprise customers’ security reviews are.

It depends on three things:

• Whether you use hosted models through APIs or need fine-tuned ones
• How much data work is needed
• How strict your enterprise customers’ security requirements are

We work in stages, and a typical path takes about 6 to 12 weeks. Every stage gives you something you can use, so you can stop after any of them.

It will be needed, but it has to be fast. Before building any solution, we analyze your tech stack, data, and customer segments and provide recommendations based on whether your use case can survive in production, its rough cost, and how to get there as efficiently as possible. And when your runway is limited, it is critical that you avoid getting stuck down the road, having invested weeks of engineering resources.

Enterprise buyers may kill a deal if they don’t understand how data-centric your solutions are. So, it is paramount to design data isolation, access control, audit trails, guardrails, an evaluation suite demonstrating efficacy, and production monitoring.

Our Harden stage (usually 2 to 4 weeks) delivers a testing suite, guardrails and a monitoring dashboard built for this kind of scrutiny. One cyber-risk platform had a performance problem blocking its first enterprise client. We fixed it in this phase, which cleared the way for onboarding, and the company went on to raise a $10M Series A.

General developers can build the app around the AI. But productizing AI brings problems they rarely run into:

• Models getting worse over time (drift)
• Different answers to the same question
• Model APIs that keep changing
• Testing and measuring quality
• Runaway running costs

We work alongside your team on exactly those parts, so you can ship a stable product faster to market than your competitors.

For a channel marketing platform, we built a RAG pipeline and automatic prompt writing. This helped them cut the time to create personalized content from weeks to minutes.

Yes. We work alongside your team and build on the model and stack you already use, whether that’s OpenAI, Anthropic, Gemini or open-source models. We only tune or swap a model when production really needs it.

You get senior engineers who have already solved the hard parts of production AI. Things like building data pipelines, picking and fine-tuning the right models, setting up evals and guardrails, and keeping cost and latency under control. They’ve done it before, so you don’t have to burn months on trial and error.

And at every stage, you walk away with something concrete: A clickable prototype and a guide to how it should work. A working pipeline running on your real data. A testing suite with guardrails and monitoring. And every sprint, you get a cost and latency report so you always know where things stand. The best part? You own everything we build. No black box, no awkward handoff. It’s yours.

Yes. Everything we build for you is yours outright. That includes code, models, pipelines and documentation. There’s no black-box handoff and no licensing lock-in.