ThinkDebug
Practical deep dives on software architecture, AI systems, and scaling digital products - drawn from the work we ship.
Retrieval, agents and LLM features wired into your product with evaluation and guardrails.
Most LLM demos work once. We build the parts that make them dependable: retrieval over your own data, evaluation you can point at, guardrails, cost control and a fallback for when the model is wrong.
Most LLM demos work once. We build the parts that make them dependable: retrieval over your own data, evaluation you can point at, guardrails, cost control and a fallback for when the model is wrong.
Your documents, tickets and catalogue turned into a retrieval layer the model answers from, with citations.
Summarisation, drafting, extraction and classification built into your existing product rather than bolted beside it.
Multi-step tasks with tool access, run inside boundaries you set and with a human checkpoint where it matters.
Test sets, scoring and tracing so you can prove a change improved things instead of hoping.
Token budgets, caching and model routing so a feature is affordable at your actual volume.
Choosing between hosted and open models on evidence, and moving between them without a rewrite.
Not sure which approach fits? A 30-minute call usually settles it.
Talk to an engineerMost of our work comes from clients we already shipped for. These are the reasons they give.
Every feature ships with a scored test set, so quality is a number you can track rather than an impression.
We size token spend against your real volume before you commit, not after the first invoice.
Refusals, PII handling and output validation designed in, not added after an incident.
Features live inside your codebase and your deployment, not in a separate tool nobody maintains.
The people who build your retrieval layer also run your infrastructure.
One feature that works beats five that occasionally do.
The parts clients ask about after the demo: who owns the output, who has access to what, and what happens once generative ai & llm integration is delivered.
Send us the scope and we come back with an itemised estimate - no discovery fee, no obligation.
We have shipped generative ai & llm integration in these sectors before, so we already know where the regulatory, integration and scale problems tend to hide.
Tell us about your industryMulti-tenant platforms, billing and SSO integrations.
Payments, KYC and ledgers built to withstand audit.
Patient portals and clinical tools built to compliance.
Storefronts and checkout tuned for conversion at scale.
Learning platforms, assessments and cohort management.
Fleet, warehouse and tracking systems that stay accurate when volume spikes.
See the full list and the work behind each one.
Explore all industriesChosen for fit and long-term maintainability, never for novelty.
Hosted and open, chosen on evidence.
Where grounded answers come from.
Serving, tracing and cost control.
Transparent stages, with something you can look at from the second one onward.
Which task is worth an LLM and which is better as ordinary code.
A test set built from your data before any model is chosen.
Chunking, embedding and indexing over your sources.
Inside your product, behind your auth, in your deployment.
Validation, refusals and per-request tracing.
Token spend and scores reviewed against the baseline.
Let's start with you.
Your information stays private. We reply within 24 hours.
Still have a question we have not answered?
Ask an engineer
Describe the task. We will tell you whether it needs a model at all, and what it would take to prove it works.
Reply within 24 hours. NDA on request. No obligation to proceed.
