AI DEVELOPMENT
AI that earns its place in the product.
We build AI that earns its place in the product. That means retrieval pipelines that return the right answer, classification that holds up under real load, and agents that do genuine work instead of generating plausible nonsense. We'll tell you when AI is the right tool — and, more often, when a deterministic rule will do the job better and cheaper.
WHAT YOU GET
AI that works on real data, at production scale.
Every AI engagement ends with a production-deployed feature with full observability — cost, latency, and output quality monitored from day one.
Timeline
4–10 weeks
Starting at
$18,000
HOW IT WORKS
Evaluate first. Build second.
We define success criteria before we write code. AI features that aren't evaluated aren't finished.
Week 1
Feasibility assessment
We assess whether AI is genuinely the right tool for the problem. If it's not — if a well-designed rule or a simpler data lookup would work better and cheaper — we'll say so.
Output: Feasibility report with recommended approach and rationale
- Problem framing: what AI actually needs to do
- Build vs. API vs. fine-tuning recommendation
- Cost and latency modelling at production scale
- Honest assessment of what AI can and can't do here
Weeks 1–2
Data audit and model selection
We identify available data, evaluate existing models and APIs, and define the evaluation criteria before writing a line of code.
Output: Model selection recommendation and evaluation criteria defined
- Data availability and quality audit
- Evaluation of frontier models vs. open-source alternatives
- Success criteria and evaluation harness design
- Cost projection for training or API usage at scale
Weeks 2–4
Prototype and evaluation
We build a working prototype and evaluate it against real inputs — not cherry-picked examples. If it doesn't work on realistic data, we iterate before building production infrastructure.
Output: Working prototype with documented evaluation results
- Functional prototype against real data
- Evaluation harness running at every iteration
- Prompt engineering and retrieval pipeline refinement
- Guardrail design for out-of-scope inputs
Weeks 4–8
Production engineering
We harden the system: error handling, cost controls, latency optimisation, and output guardrails. AI that works in a demo but breaks at scale is worse than no AI.
Output: Production-ready AI system with monitoring hooks
- Error handling and graceful fallback logic
- Cost control: token budgets and caching strategy
- Latency optimisation for real user response times
- Output guardrails and content filtering
Weeks 8–10
Monitoring and deployment
We set up cost tracking, output quality monitoring, and alert pipelines before going live. AI systems degrade silently — you need observability from day one.
Output: Live system with cost, latency, and quality monitoring in place
- Cost monitoring per request and per user
- Output quality evaluation pipeline
- Alerting for latency spikes and error rates
- Deployment to production with rollback capability
CLIENT STORY
AI that ships.
Stackform needed AI features that would survive real usage — not a polished demo that fell apart under load.
COMMON QUESTIONS
AI development FAQ.
When AI genuinely changes the user outcome — when it can do something a rule or a search index can't. Not when it's added to improve positioning. We'll tell you if it's the wrong call for your specific problem.
Usually yes. Building on a frontier model API is faster, cheaper, and more capable than custom training for most product use cases. We only recommend custom training when the specific use case demands it — and we'll explain exactly why.
Cost controls, output guardrails, fallback handling, latency within acceptable bounds, and a continuous evaluation pipeline. A feature that works in a demo but fails silently at scale isn't production-ready — it's a liability.
We define success criteria before building, build an evaluation harness alongside the feature, and run regular evals throughout development — not just at the end. You get evaluation results at every milestone.
Not always. Many of our most effective AI features use retrieval, prompting, and careful architecture — no fine-tuning required. We'll tell you upfront if your use case genuinely needs training data.
We prompt first. Fine-tuning has real costs — data collection, compute, maintenance, and the risk of degradation over time. It's worth it in specific situations. We'll tell you when it is and when it isn't, with numbers.
Have an AI idea worth building?
Free 30-min call. We'll tell you honestly if it's viable — and what it'll take.