AI Application Development

How we approach AI projects

Of the AI requests we have seen over the past two years, roughly half stalled right after the proof of concept. Not because the models were inadequate, but because of the same few things: nobody could say whether the answers were right, nobody could forecast the cost, and updating the knowledge meant redoing the work.

So our order of operations is unusual: build the evaluation set, then write the code.

1. Define the scenario. Turn “use AI to improve efficiency” into specific tasks whose answers can be judged right or wrong.
2. Build the eval set. Collect 100-300 real questions with expected answers as the quality baseline. Without it, every later optimization is guesswork.
3. Run a PoC. Two to four weeks to close the loop end to end, producing three numbers: accuracy, latency and per-call cost.
4. Decide. If the numbers don’t clear the bar, change the approach or stop — rather than pushing on out of momentum.
5. Engineer it. Permissions, auditing, monitoring and canary release turn it into an actual system.

Main areas

Knowledge bases and RAG

Turning knowledge scattered across documents, tickets, wikis and databases into something you can simply ask. The hard part is rarely calling the model — it is retrieval quality: chunking strategy, hybrid search (vector plus keyword), reranking, citation, permission filtering. Every answer must open onto its source, or the business will never trust it.

Support copilots

Two shapes: fully automated responses (good for high-volume standard questions) and agent assist (AI drafts, a human confirms and sends). The second meets far less resistance and accumulates better training data, so we usually suggest starting there.

Content understanding and moderation

Automatic tagging, summarization, classification and compliance review for video, images and text. This connects directly to our video transcoding pipeline — frames and audio are extracted as a side branch instead of building separate ingestion.

Agents and workflow automation

Letting the model call your internal APIs to complete multi-step tasks: look up orders, route approvals, generate reports, update tickets. The crux is permission boundaries and reversibility — we design high-risk actions as “AI proposes, human confirms” rather than direct execution.

Private deployment and fine-tuning

Where data cannot leave the network, we provide open-model deployment plans including GPU sizing and concurrency estimates, plus fine-tuning and distillation on your own corpus. Note: for most scenarios, solid retrieval and prompt engineering are enough — fine-tuning is a last resort, not a starting point.

On model selection

We are not tied to any model vendor. Under the same application architecture, hosted APIs and self-deployed open models remain interchangeable, and selection follows three measured numbers: accuracy on the eval set, P95 latency, and cost per call. Compliance requirements — data residency, regulatory constraints — are usually the first constraint, and we make them explicit during the design phase.

About cost

AI applications have a cost structure unlike traditional systems: it grows linearly with usage. Every proposal we write includes a per-call token cost estimate and a monthly budget range, backed by three engineering measures: caching frequent answers, routing by task difficulty to differently sized models, and controlling context length. Together these typically cut the bill by more than half.

Indicative budget

Want to know whether your scenario is a good fit? We offer a free scoping session — get in touch.

What you receive

• Scenario feasibility assessment and PoC report
• Evaluation set and quality baseline
• Full application source code and prompt engineering assets
• Knowledge ingestion and refresh pipeline
• Cost model: per-call cost and monthly budget projection
• Post-launch quality dashboard and iteration process

Related case studies