Generative AI Development Services

Plan in phases, not in a single number. Discovery and feasibility typically take two to four weeks. A pilot in production with real users takes eight to 12 weeks. A full production build usually runs three to six months from that point. Anyone quoting a total timeline before seeing the data is quoting a guess. What each phase actually delivers:

1. Discovery, two to four weeks. Data audit, feasibility, architecture, and an evaluation plan. The main output is often a decision not to build something, and that decision is cheap here and expensive later.
2. Pilot, eight to 12 weeks. One workflow, real users, measured against a baseline. Not a demo. A demo proves the model can produce a good answer once; a pilot proves it does so reliably on live input.
3. Production, three to six months. Integrations, evaluation harness, monitoring, cost controls, access permissions, and handover.

What extends the timeline, ranked by how often it happens: data that is not where it was said to be, a success criterion nobody agreed on before the build, security and procurement review, and integration with systems whose owners were not in the kickoff. What compresses it: one workflow instead of five, a documented process, a named decision-maker, and access to the data on day one rather than in week six.

Buy off-the-shelf when the workflow is standard and the tool already does it. Build custom when the workflow is specific to your business, when the system needs your private data, when the output must integrate deeply with internal systems, or when the process itself is the competitive advantage. If a subscription tool solves the problem, building is the wrong decision. Buy when:

• The workflow is common across companies (support deflection, meeting notes, document search).
• The data can leave your environment under a standard agreement.
• Time to value matters more than fit.
• The process is not a differentiator, and doing it the same way as everyone else costs you nothing.

Build when:

• The workflow depends on proprietary data, proprietary logic, or a process that is genuinely yours.
• Off-the-shelf tools get you most of the way and stall, which is the most common trigger and usually appears in month four of a subscription.
• Compliance or data residency rules out the vendor's hosting model.
• The economics invert at scale: per-seat pricing that was reasonable for 50 users becomes indefensible at 5,000.
• The system must act inside your stack, not adjacent to it.

The honest test.

Write down what the off-the-shelf tool cannot do, then ask whether that gap is worth a six-figure engineering project. If the gap is convenience, buy. If the gap is capability that changes an economic outcome, build. Most companies should do both: buy for the commodity workflows, build for the two or three that actually differentiate them.

A complete engagement covers six things: discovery and feasibility, architecture and model selection, the data and retrieval layer, the application and integrations, an evaluation harness that proves it works, and the operations layer that keeps it working. The build is the visible part. Evaluation and operations are where most of the effort goes. What each piece means in practice:

• Discovery. Data audit, use case assessment, architecture, cost model, and a written success criterion. The stage that most often prevents an expensive mistake.
• Architecture and model selection. Which model, hosted where, with what fallback. Chosen against your constraints on cost, latency, and data residency, not against a benchmark leaderboard.
• Data and retrieval layer. Ingestion, chunking, indexing, permissions, and the grounding rules that force exact values to come from records rather than from generated text.
• Application and integrations. The product itself, and the connections into the systems it has to read from and write to.
• Evaluation. A repeatable test set, run before every release, so quality is measured rather than asserted. This is the line between an engineering firm and a prototype shop.
• Operations. Monitoring, drift detection, cost per transaction, logging, and incident response.

What is frequently not included, and should be asked about explicitly: the cost to run the system after launch, data quality remediation, retraining or re-evaluation when the underlying model is updated, and handover documentation good enough for your team to operate it alone.