AI software development company,
built operator-grade.

Most credible AI software development companies do four things: scope which AI workflows are worth building (audit), build them against your real systems (pilot), run them in production with monitoring + drift detection (continuous), and tell you when AI is the wrong answer (where forecasting, rule-based systems, or a SaaS tool will outperform an LLM call). We do all four. The shape of a good AI development engagement is rarely "build me a chatbot" — it's rebuild the eval set, pick the right model per workload, ship one workflow end-to-end with a kill point, and report on cost-of-ownership monthly afterward.

Read the vendor rubric above — the 10 criteria are the ones we'd grade ourselves on. The short version: a credible AI development company builds the eval suite first, picks models per workflow rather than per partner badge, will project token cost at steady state before you sign, and will tell you when an LLM is the wrong answer. Top AI development companies show you anonymized capability patterns with real metrics, not stock-logo client grids. They publish pricing. They name the engineers you'll actually work with. They have public open-source work you can verify on GitHub. If a vendor can't answer "what's your eval methodology?" without pivoting to a slide, that's the answer.

AI consulting is strategy: we audit your existing workload (or evaluate a future one), recommend which model fits each use case, project token costs, and give you a 90-day implementation roadmap. We deliver a document, not code. <a href="/services/ai-consulting/">AI consulting and development</a> together is the most common path — most teams start with a one-week audit ($3K) to scope what's worth building, then move into a development pilot ($10–25K) for the highest-ROI workflow. Some teams already know what they want shipped and skip the audit. Both paths are fine — the audit is a fit-test, not a gate.

Both. Generative AI development services is the umbrella: production GenAI applications (copilots, drafting tools, summarizers), structured-extraction pipelines on images and documents, LLM agents that take actions against real systems, voice agents on the OpenAI Realtime API, and RAG over your private corpus. LLM development services specifically is the deeper engineering of the agent layer — tool-use schemas, multi-step orchestration, prompt caching, eval suites, and drift monitoring. We treat them as one engagement; you don't have to pick.

Yes. AI app development services is one of our most-shipped patterns. We build your AI app end-to-end — frontend (Next.js, Astro, or Flutter when a single codebase needs to ship mobile + web), backend (FastAPI or Node), retrieval (pgvector or Pinecone), agent layer (Claude or GPT), auth/billing, and telemetry. The AI part is one variable in a normal app build; we don't treat it as a separate workstream that needs its own vendor. An AI app development company that can't ship the actual app is selling consulting under a different name.

Some workloads aren't a fit for an LLM call — they need classical machine learning. Forecasting (sales, demand, inventory), recommendation systems, anomaly detection, computer vision on edge devices, and high-volume narrow classification all typically belong in XGBoost, scikit-learn, or PyTorch territory rather than "prompt a model." Machine learning development services is where we build those. We often ship hybrid systems: ML for the structured-signal majority, an LLM for the gray-band cases the classifier isn't confident on (see the marketplace fraud capability pattern above). The point is to use the cheaper tool when it works and only escalate to an LLM when the cheaper tool can't.

Three engagement tiers, no surprises. A one-week audit is $3,000 — workflow inventory, model recommendation per workflow, token-cost projection, stack recommendation, 90-day roadmap. A pilot is $10,000–$25,000 fixed price, 5–8 weeks — one AI workflow shipped end-to-end with eval, monitoring, and runbook. A continuous AI team is from $5,000 per month — embedded PM + AI engineers shipping integrations on your roadmap with monthly cost-of-ownership reporting. Per-workflow run cost at steady state typically lands at $200–$1,500/month depending on volume and which model tier the workflow uses. If you need to hire AI developers as a standalone capacity (no scope, just heads), we're not the best fit — we're an outcome-priced operator team, not a staffing shop.

Three honest cases. (1) The workflow doesn't need an LLM — rule engines, structured ETL, or a SaaS tool will outperform an AI call at a fraction of the run cost. We'll tell you in the audit if that's you. (2) You don't have the data yet — if the eval set can't be built because the underlying business process isn't measured, the AI build is premature. Fix the measurement first. (3) You need an in-house AI org built. A vendor (us included) is the wrong shape; you need a head of AI engineering and a hiring plan, and we'd recommend an executive search firm. The honest answer to "should we hire an AI development company?" is "sometimes." We're glad to be one of those times — but not when we're not.