AI Solutions for Business
Practical AI features that earn their place - useful, private, and dependable.
The gap in AI right now isn't ideas - it's the distance between a demo that wows in a meeting and a feature that's reliable, affordable, and safe in front of real customers. We close that gap. We start from the actual workflow, ground the model in your data, and engineer for accuracy, latency, and cost with real evaluation - so what ships keeps working long after the applause stops.
Most AI demos never reach production.
It's easy to wire up an impressive prototype and hard to make it reliable, affordable, and safe in front of real customers. Accuracy drifts, latency creeps, and costs surprise everyone.
How we deliver.
Start from the workflow
We scope the actual job to be done and where AI genuinely helps - not where it just looks impressive.
Right model & retrieval
We pick the model and grounding strategy per use case, balancing quality, latency, and cost.
Evaluate & guard
Evaluation sets and guardrails so behaviour holds up before - and after - it reaches customers.
Ship to production
Monitoring, fallbacks, and cost controls so it runs reliably at scale.
Everything to launch and grow.
Products we've shipped.
Read before you build.
Your app depends on a model with a retirement date
Every model you build on has a published expiry. Eight Claude model IDs stopped serving requests in the last year - here's how to make the swap routine.
AIWhat the EU AI Act actually requires on 2 August 2026
On 2 August the AI Act's transparency rules and its fines both switch on. The high-risk rules didn't - they slipped to 2027. Here's what really applies.
AIThe unit economics of an AI feature
Token prices fell ~50x a year, yet AI bills keep climbing. The thing that costs money isn't a token - it's a task, and agentic tasks use 1000x more of them.
StrategyFine-tune, RAG, or prompt? Reach for them in this order
Prompting changes instructions, RAG changes knowledge, fine-tuning changes behavior. Prompt first, add RAG for your data, fine-tune only when both fall short.
EngineeringGetting reliable JSON out of an LLM (stop parsing hope)
Prompting for JSON fails 5-10% of the time; schema-enforced structured output is ~100% valid via constrained decoding. Use it, and still validate in code.
StrategyAI-native vs AI-as-a-feature: which are you building?
Remove the AI - does your product still work? If yes it's a feature; if the business collapses it's AI-native. That gap decides defensibility, not the model.
EngineeringPrompt injection is the new SQL injection - but unsolved
Prompt injection is OWASP's #1 LLM risk. It shares SQL injection's root cause - data becoming commands - but has no parameterized-query fix. So you contain it.
EngineeringYou can't ship AI you can't evaluate
AI features are non-deterministic, so 'it looked right in the demo' isn't a test. Evals - a dataset plus graders, run in CI and prod - are how you ship them.
AIRepository intelligence: your codebase is the context now
AI coding moved from autocompleting lines to understanding whole repos - and running several agents on one task. Here's what changes for how teams build.
AIThe delegation gap: why AI does most of the work but runs little of it
Developers use AI in ~60% of their work but can fully delegate only 0-20% of tasks. That gap - not the hype - is what actually shapes AI-era engineering.
AIOne agent isn't enough: when to use multiple AI agents
Multi-agent systems beat a single model by ~90% on the right tasks - and burn 15x the tokens on the wrong ones. Here's how to tell which is which.
EngineeringThe hidden bill of AI-generated code
AI assistants ship code fast - and technical debt and leaked secrets behind it. Winning teams treat the assistant as an untrusted contributor, not a colleague.
AIYou don't need a frontier model: the case for small language models
Most teams pay frontier prices for work a small model does just as well. The 2026 move is right-sizing - SLM-first, escalate only the hard part.
AIContext engineering: the real skill behind AI agents that work
The teams whose AI features work aren't using better models - they're feeding them better context. Why context quality, not size, is the 2026 bottleneck.
AICoding agents in 2026: what SWE-bench scores really mean
Coding agents went from 2% to ~94% on SWE-bench in three years. Here's what those scores really mean - and where the agents still need a human.
AIHow AI agents remember: memory systems in 2026
How AI agents remember in 2026: the four memory types, the tiered architecture that beats a growing transcript, and when your agent actually needs memory.
AIIs RAG dead? RAG vs long context in 2026
What RAG is really for in 2026, when a long-context window beats it, and why naive RAG died while agentic retrieval is thriving.
AIWhat an AI agent actually is (and what makes one reliable)
A plain-English anatomy of an AI agent: model, tools, memory, context, and the loop, plus applications and why most agents fail on state, not smarts.
AIDoes your business actually need AI? A 2026 reality check
A neutral guide to when AI is worth it for your business in 2026: why most projects fail, where AI genuinely helps, and how to tell the difference.
AIAgentic AI's real challenge isn't the demo - it's production
Gartner says 40% of enterprise apps will embed AI agents by end of 2026, yet fewer than 1 in 4 teams have scaled one to production. Here's why, and how to close the gap.
AIOn-device AI is quietly winning - and it's a gift for privacy
Over 2 billion phones now run local language models, NPUs are standard, and small models are 10–30× cheaper to run. Why the future of AI is increasingly local.
EngineeringAI coding agents changed how we build - not whether craft matters
Command-line coding agents are shipping real engineering work 30% faster. What that actually changes for a software studio, and what it doesn't.
Questions, answered.
Which AI models do you build on?
We use the most capable current models and choose per use case for quality, latency, and cost - and we keep the design flexible so you can switch as the landscape moves.
Can you work with our existing data?
Yes. We integrate your data securely with retrieval and strict access controls, so the model only sees what it should.
How do you keep it accurate?
We build an evaluation set up front and measure every change against it, so quality is tracked, not assumed.
Is our data used to train models?
No. We configure providers so your data isn't used for training, and we keep sensitive data on paths you control.
Have a project in mind?
Tell us what you're building. We'll come back with a senior read on how to ship it.