Most companies do not need a moonshot. They need the demo that impressed everyone to become a system that holds up under real traffic, real costs and a real audit — which is a different engineering problem, and the one this pod is built for.
Four weeks for generative and LLM work — the only published time-to-first-release on the engineering side.
The EU AI Act, the NIST AI Risk Management Framework and ISO/IEC 42001, which the governance work maps evidence to.
Stated in the group security ledger, and the reason self-hosted and VPC-isolated deployment is offered.
Every engineer is evaluated through the group's own platform before they reach you.
Appsierra designs, builds and operates production LLM and ML systems: agents and copilots that use tools under human supervision, retrieval pipelines grounded in your own data, fine-tuned or adapted models, and the LLMOps layer that keeps all of it accurate, fast and affordable. The pod is an AI engineer, an SDET and a platform engineer — the test engineer is in it by default, because the evaluation gate has to be staffed rather than assumed.
The distinctive deliverable is the control plane. Agents ship with an Agent Operation Centre: dashboards to monitor every run, inspect the decisions and tool calls, replay traces, set limits, and pause or roll back behaviour. Around them sit scoped permissions, typed and validated tool inputs and outputs, and guardrails tested under adversarial pressure — prompt injection and jailbreak red-teaming, not a policy document.
Quality is measured, not asserted. Evaluation sets are built from your real prompts and edge cases, versioned like code, scored automatically and reviewed by people, then wired into CI as a gate. Agentic work is held to task success rate, generative work to a faithfulness score, and governance work to gate pass rate. Published time to a first release is six weeks for agentic systems and four for generative ones.
What the pod actually hands over. Retrieval quality and the evaluation harness are where most of the value sits, and both are unglamorous.
Document pipelines, a chunking strategy chosen rather than defaulted, embeddings and a tuned vector store on pgvector, Pinecone or Weaviate. Good retrieval is usually the single biggest driver of answer quality, ahead of the model choice.
Goals, a reasoning loop, tools, memory and — critically — explicit stop conditions, shipped inside a maintainable codebase. Multi-agent work runs a planner, a researcher and a tool-runner under an orchestrator with shared memory and defined handoffs.
A control plane for running agents: monitor every run, inspect decisions and tool calls, replay a trace, set limits, and pause or roll back behaviour. No other service in the group ships an operational surface like this.
Dataset preparation, parameter-efficient tuning with LoRA, and a rigorous before-and-after evaluation so the adapted model is genuinely better rather than merely different. RAG is the default where knowledge changes; tuning is for behaviour the base model does not have.
Versioned prompts and models treated as engineering artefacts, automated evaluation gates in CI, observability across quality, latency and token spend, safe rollback, and token budgets set per feature so cost is a design constraint rather than a monthly surprise.
Scoped permissions, input filtering, output validation and policy enforcement, tested by deliberately trying to break the system with prompt injection, jailbreaks and edge cases before real users or attackers find them.
The engagement shape is the same across the engineering practice; what changes here is what gets measured.
What exists, what it has to do, and which of the three shapes it is: a retrieval problem, a behaviour problem or a governance problem. Pricing is quoted after this, once the scope is real.
An AI engineer, an SDET and a platform engineer, drawn from a pre-vetted bench and productive in about seven days. You interview them first, and nothing is subcontracted without your written consent.
Golden datasets from your real prompts and edge cases, versioned, with automated scoring and human review. Without this there is no way to tell whether the next change helped, which is why it comes before the model work.
The pipeline, prompts, retrieval and guardrails go in together with the evaluation gate in CI, so a release either passes on score movement or does not go. Cost and latency are observed from the first deploy, not after the first bill.
The Agent Operation Centre, the runbooks and the evaluation sets are yours. Exit is documented rather than negotiated: no lock-in, and IP transfers at the end.
Four buyers, and each arrives from a different failure — which is why the first call is about the failure rather than the technology.
The demo worked and nothing has reached production. What is missing is usually not model capability but the evaluation, the guardrails and the operational surface that make a release decision possible.
A prototype that hallucinates, costs more than expected and frustrates users at the tail of the latency distribution. All three are addressed by measurement rather than by a bigger model.
AI regulation arriving faster than the internal portfolio can be documented — turning a sprawl of experiments and shadow AI into a managed portfolio with evidence mapped to the EU AI Act, NIST AI RMF and ISO/IEC 42001.
When AI ships inside your product, every regression is customer-facing. The gate on score movement is what stops a prompt change from becoming a support queue.
The scope fence, and one thing the practice refuses to sell.
Use retrieval when answers depend on changing or proprietary knowledge, and fine-tune when you need a consistent tone, format or task behaviour the base model does not have. Many production systems do both, and the choice is made against cost and accuracy targets rather than fashion.
We are model-agnostic and select on accuracy, latency, privacy and cost. That includes leading commercial models such as Claude and GPT, and open models you can self-host — benchmarked against your task before anything is committed to.
No. Your data never trains a shared model. AI governance is ISO 42001-aligned, every AI decision carries its evidence for an audit, and self-hosted or VPC-isolated deployment is available where your policy requires it.
Every agent ships with guardrails, an evaluation harness and human-in-the-loop checkpoints. It operates within scoped permissions, decisions are logged, high-impact actions are reviewed by a person, and the Agent Operation Centre can pause or roll back behaviour mid-flight.
Model size is evaluated against the task, simple requests are routed to cheaper models, prompts and context windows are optimised, results are cached and batched where possible, and token spend is budgeted per feature and monitored continuously.
Adversarial testing where engineers deliberately try to break the system — prompt injection, jailbreaks and edge cases — to surface unsafe, biased or non-compliant outputs before real users or attackers do. It runs against your guardrails, not against a generic benchmark.
Talk to the group and a senior lead scopes it in writing, or go straight to the service's own site and look at it yourself. Neither route commits you to the other.
Name the number you need to hit. A senior lead replies within one business day and a costed plan follows within three working days.
Appsierra's own site, where this splits into four detailed pages: AI and ML, agentic AI, generative AI and AI governance and evaluation.
Everything the group sells around AI & ML engineering — the company that delivers it, the nearest siblings, and the full list.