AI & Agentic Systems Development
An AI development company focused on agentic systems that survive production: LLM integration, tool use, and reliability engineering, applied with the discipline of a payments team.
AI systems we build
We build LLM-powered systems intended to run without a human watching: agents that execute multi-step workflows with tool use, document and data pipelines doing extraction and classification, retrieval systems grounded in your own data, and AI features embedded in production products. The bar we build to is unattended operation — a demo that works while supervised is the starting line, not the deliverable.
Our sweet spot is AI in financial contexts, where our payments background compounds: agents that triage reconciliation exceptions, monitor operations, draft compliance reports, or act as the intelligence layer over a ledger. We run this internally too — an autonomous research-and-writing pipeline produces this site's technical blog under human review — and the lessons from operating it feed directly into client work.
Reliability engineering for agentic systems
The gap between an impressive demo and a dependable system is engineering, not prompting. We treat model output as untrusted input: schema-validated, bounded by allowlisted tools, and checked against explicit invariants before anything irreversible happens. Long-horizon agents get checkpoints, budgets, and resumable state, so a failure at step forty does not restart the work from step one.
Evaluation is the core practice. Before shipping, we build eval suites from real cases and measure every change against them — model swaps, prompt revisions, tool changes — the way conventional software relies on regression tests. In production, every agent action is logged and traceable, so when behavior drifts you have evidence rather than anecdotes.
Why CodeDecoders for AI development
We bring systems discipline from a harder domain. Our payment infrastructure processes real money — $100M+ settled — under constraints where retries must be idempotent and every state change must be auditable. Those habits transfer directly to agents, which fail in the same ways distributed systems do: partial completion, duplicate actions, and silent drift.
We are model-pragmatic: Claude, GPT, Gemini, or open-weight models, chosen per task on measured quality, latency, and cost rather than loyalty. Because we build the evals first, swapping models later is an afternoon of measurement instead of a leap of faith. Vendor churn is constant in AI; your architecture should not care.
Engagement model
Most AI engagements start with a two-to-four-week build of one working slice: a single agent or pipeline running on your real data, with an eval suite proving what it does. That artifact settles the build-versus-buy and model-choice debates with numbers, and it either earns the next phase or saves you from a bad quarter-long bet.
From there we scale to production — hardening, monitoring, and cost controls — either as a fixed-scope delivery or embedded alongside your team. You own the code, the prompts, the eval suites, and the operational playbooks. As models improve, the eval harness is what lets you adopt them quickly; we consider it the most valuable thing we leave behind.
Models
Agent tooling
Backend
Common questions
What does it cost to build an AI agent or LLM feature?+
A scoped first build — one agent or pipeline on real data, with an eval suite — is typically a few weeks of senior engineering, quoted fixed after scoping. Production hardening is a second phase priced on what the pilot reveals. Ongoing model API costs are separate, and we design explicitly to keep them controlled.
Which models do you work with?+
Claude, OpenAI's GPT models, Gemini, and open-weight models when data control or cost demands them. We choose per task using evals on your actual workload, and we architect so the model is a swappable component. In practice most systems end up multi-model: a strong model where judgment matters and a cheaper one for volume.
How do you make agents reliable enough for real work?+
By treating them as distributed systems rather than chatbots: validated outputs, allowlisted tools, idempotent actions, budgets, checkpoints, and human approval gates wherever actions are irreversible. We measure reliability with eval suites built from real cases, before and after every change. Perfection is not available, but bounded, auditable failure is.
Can AI agents really handle financial workflows?+
Yes, with the right boundaries. Reconciliation triage, operations monitoring, report drafting, and anomaly investigation are proven fits: the agent does the reading and correlation, while constraints or humans gate anything that moves money. Our double-entry ledger background is what makes those boundaries enforceable rather than aspirational.
What happens to our data — does it train someone else's model?+
No. We use API tiers with no-training guarantees, and when requirements are stricter we deploy open-weight models inside your cloud so data never leaves your infrastructure. Data handling is part of the architecture review at the start of every engagement, not an afterthought.
Building ai & agentic systems? Let's scope it.
We scope engagements in days, not weeks. Send a few lines about your project and we respond within 24 hours.
Let's build something extraordinary together.
Free consultation·Response within 24h·No commitment
info@codedecoders.io