Every engagement starts by asking where intelligence genuinely helps. LLM pipelines, agentic workflows, and AI features that replace real manual overhead.
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See All Projects →Getting GPT-4 to do something is easy. Getting it to do the same thing reliably, at scale, without your costs spiraling or the model quietly changing under you, is the hard part. That is the part we build.
There are models that fit specific jobs better than others. Here is how we pick.
Most production systems we build use two or three of these together. A cheap fast model for the easy work, a smarter model for the hard cases, and an open-source fallback for sensitive data.
The architecture you pick is more important than the model. Three patterns cover most of what we build.
You send a prompt, you get a response. Good for stateless, generic tasks where the model already knows what it needs. Summarisation, simple classification, text rewriting, format conversion. Cheapest to ship. Fragile at edge cases.
When the model needs to answer based on your specific data. We retrieve the relevant context, feed it to the model, and ground the answer in your data. Done well, virtually eliminates hallucination. Done badly, confident-sounding nonsense with citations.
When you need a specific style, tone, or capability the base model cannot reliably produce. More expensive, more involved, and you now manage a custom model. We recommend it sparingly — after the first two patterns have been tried.
We have seen these enough times to flag them before they happen to you.
The model invents an answer and presents it like fact. Often undetectable without domain expertise.
→ Grounding in real data · designing the system to refuse rather than guess.
A feature that worked fine in testing costs thousands a month at real scale.
→ Caching repeated queries · right model per task · spend controls per tenant or user.
Two seconds is fine in chat. Unacceptable in a real-time search box or product flow.
→ Streaming responses · prefetching · faster models on latency-sensitive paths.
The provider updates the model. Prompts that worked now produce different output.
→ Pinning to specific model versions · evaluation sets that detect drift · upgrading on your schedule.
OpenAI goes down. Anthropic has an incident. Your AI feature stops working.
→ Fallback model from a different provider · graceful degradation that does not look broken.
The gap between a demo that wows and a feature that holds up is mostly the engineering around the model, not the model itself.
Repeated identical queries should not hit the model twice. Saves cost, reduces latency. Trickier than it sounds when prompts include user data.
Responses appearing as they generate, rather than waiting for the full answer, transforms perceived speed. Non-negotiable for user-facing features.
Primary provider down or rate-limited — fall back to another. The user does not notice. The on-call engineer is not paged at 3am.
Every prompt, response, latency, and cost logged. Traces so you can debug what happened on that one weird call. Evaluation runs against a golden set.
Per-user, per-tenant, per-feature spend limits with alerts before things get expensive. Non-negotiable at any meaningful scale.
Prompts are code. Version-controlled, tested, and rollable like any other code. This is where most side projects differ from production features.
LLM providers have come a long way on enterprise data handling, but the details matter and they change.
OpenAI, Anthropic, and Google all offer enterprise plans where your data is not used for training, with zero-retention options. We help you negotiate the right tier and verify the contract terms match your compliance needs.
Open-source models (Llama, Mistral, Qwen) on AWS Bedrock, Vertex AI, or self-hosted in your VPC. Your data never leaves your environment. We recommend this when privacy, compliance, or cost makes it the right call.
For healthcare, finance, and legal, we build with the assumption that audit, encryption, and data residency are not afterthoughts. We have built systems satisfying these requirements with LLM features inside them.
Chosen based on project requirements — not defaulted to the most popular option.
We are documenting recent work — covering the problem, the architecture, and the measurable result.
GPT-4o for the smart work, GPT-4o mini for high-volume classification, and Llama as the privacy-preserving fallback. Three models, one coherent system.
A retrieval-augmented LLM feature built on internal documents, with citations and refusal patterns to eliminate hallucination.
An LLM feature in a real-time product flow where streaming, caching, and prefetching mattered more than the model choice.
Depends on the job. For most general production work, GPT-4o is a sensible default. For long-document analysis or careful reasoning, Claude. For multimodal or huge context, Gemini. For privacy and cost at scale, open-source like Llama. We will recommend based on what you are actually building.
The build cost is usually a few weeks of work for a focused integration, more for a system spanning multiple features. The interesting number is the running cost, which depends on usage volume, model choice, and how well the system is engineered. We model the per-month cost as part of scoping so there are no surprises.
When it helps. LangChain is useful for orchestration-heavy projects, particularly agentic workflows. For simpler integrations, direct SDK calls are often cleaner and easier to debug. We pick based on the project, not loyalty to a framework.
Yes. We deploy open-source models (Llama, Mistral, Qwen) on AWS Bedrock, Vertex AI, or self-hosted in your VPC. Your data stays in your environment. We will recommend this when privacy, compliance, or cost makes it the right call.
Your prompts may produce different output. We protect against this by pinning to specific model versions where possible, running evaluation sets that detect drift, and upgrading on your schedule rather than reacting in panic. The change is not a surprise.
RAG retrieves relevant context at query time and grounds the model’s answer in your data — no training required, works immediately on new data. Fine-tuning bakes a specific style or capability into the model through additional training. We default to RAG and recommend fine-tuning sparingly, after RAG has been tried.