20 ways in, grouped by what you are trying to do. None of them is a boundary: whichever one you start from, the engagement covers the whole system.
Bigcircle picks among its AI development services by the failure the system has to survive. Every engagement gives each call only the access it needs, keeps personal data away from the model with PII tokenization, runs guardrails that check every action before it reaches your systems, and logs every step for LLM observability. You buy production hardening for a prototype that stalled, a fixed-scope build against a fixed scope and price, or a studio-led programme run by the founder as fractional CTO, and in each one you own the code.
Three ways to buy a Bigcircle service: production hardening, a fixed-scope build, or a studio-led programme. The difference is how much of the delivery Bigcircle runs.
| Criterion | Production hardening | Fixed-scope build | Studio-led programme |
|---|---|---|---|
| Who leads | You built it, we finish it | We own it | We run it |
| What you bring | A prototype that works in the demo | The problem | The roadmap and the product context |
| What you get | Your prototype taken into production, with an eval set, model routing for cost and latency, a locked-down data path, and a safety net under it. | The working system. Discovery, build, evals, and deployment under one point of accountability, against a fixed scope and price. | A studio team of 3 to 8 engineers and QA, led by the founder as fractional CTO, that owns delivery and the hard technical calls. |
| When it fits | You have a prototype that will not cross into production. | The problem is clear and you want the result, not a team to manage. | You have a roadmap and no senior AI team to run it. |
| How Bigcircle handles this | Each call gets only the access it needs, PII tokenization keeps personal data away from the model, guardrails check every action before it reaches your systems, and LLM observability logs every step. You own the code. | The same six planes. Bigcircle owns delivery: discovery, build, evals, and deployment against a fixed scope and price. You own the code. | The same six planes, with the founder leading the team as fractional CTO. You own the code. |
If more than one of these is true, they are usually one engagement rather than several. If none of them is, say so on a call and we will tell you plainly whether we are the right people.
“A process our team runs by hand every day touches four different systems.”
The work is sequential and the steps are real actions, not answers. That is agency, and it needs a critic and a guardrail policy more than it needs a better prompt.
“We hold thousands of documents and nobody trusts an answer without opening the source.”
The bottleneck is grounding, not generation. Every field has to trace to a span a reviewer can open, and anything outside the approved index has to be refused.
“It is impressive in the demo and falls over on real traffic.”
You do not need a different model, you need the layer underneath it: evals that block a bad release, routing for cost and latency, and a record of what actually happened.
“Cloud changes are slow, and the risky ones keep getting postponed.”
The fix is making changes safe to apply rather than making people braver: plan, validate, apply, and a rollback path already written.
“Customers wait on hold for answers our systems already contain.”
This is a latency problem before it is an AI problem. Under a second, grounded in live records, with a clean escalation when the agent should not be the one answering.
“The model works. The product around it does not exist yet.”
What is missing is the surface: the waiting states, the interruption handling, and the decision about what runs on the device rather than in the cloud.
Build it
You know what you want built, and you are shopping for who builds it.
For multi-step work across systems where a wrong action is expensive to undo. Agents that plan, act through MCP tools, and finish only after a separate read matches the expected state.
An AI chief-of-staff that runs operations end to endDevOps and infra agents that apply and roll back safelyWorkflow agents that automate recurring back-office work
View serviceFor teams whose answers get checked. Extraction and search where every field traces to a span in the source, and anything outside the approved index is refused.
Search and Q&A over your internal knowledgeCitation-grounded assistants for regulated workAutomated extraction from contracts, filings, and forms
View serviceFor phone lines where hold time is the cost. Sub-second voice agents grounded in live records, with every call recorded, structured and replayable.
Inbound and outbound call agents on real phone linesInsurance, real estate, and support voice linesMultilingual assistants that scale across regions
View serviceFor work that outgrew one agent. Specialist agents under a supervisor, explicit handoffs, and one trace across the whole run.
Workflows too complex for a single agent to holdResearch and analysis pipelines with specialist stagesRuns you can debug at the step level
View serviceFor answers that have to come from your data. Hybrid search, reranking and chunking tuned against a real eval set, built to refuse rather than improvise.
Question answering over internal knowledgeRetrieval that reports its own accuracyGrounded assistants that decline instead of guessing
View serviceFor teams who have a product and want AI inside it. The feature and the surface around it, built in your codebase and your release process.
An AI capability shipped inside an existing productCopilot and assistant surfaces your team can maintainA rollout you can halt without a redeploy
View serviceGet it to production
It works in a demo and falls over on real traffic.
For a demo that has to survive real traffic. Evals that block a bad release, routing that holds cost and latency under load, and a record of what actually ran.
Eval and observability platforms for your AI featuresCost and latency cut without losing qualityGoverned, audit-ready AI for regulated industries
View serviceAgent = model + harness. We build the harness: context assembly, tool dispatch, state that survives a crash, permissions, and the eval rig that proves a change is safe to ship.
An agent that survives a restart instead of losing the runTool calls that fail loudly and recover, rather than silently returning nothingA context window that stays useful on long tasks
View serviceFor teams shipping on opinion. A labelled set from your real traffic, metrics that match the task, and a gate in CI so a quality drop blocks a release.
A number to ship against instead of an opinionRegressions caught before customers find themModel and prompt changes you can compare honestly
View serviceFor teams who cannot explain what happened. Full execution traces, tool-call success rates, cost and latency per step, and drift over time.
Any run replayable end to endCost attributable to the step that spends itFailures caught by monitoring instead of by a customer
View serviceFor teams paying frontier prices for routine work. Each request class measured on cost, latency and quality, then routed to the smallest model that holds.
Inference cost reduced without a quality dropLatency held under load through fallbacksProvider changes absorbed by configuration, not a rebuild
View serviceFor systems where a bad action is expensive. Input and output validation, action policies that stop a call before it happens, and human checkpoints where stakes justify one.
Autonomy that is safe enough to leave runningPolicy enforced in code rather than in a promptAn audit trail of what was refused and why
View serviceFor teams who cannot send raw records to a provider. Encode on the way out, decode on the way back, so sensitive fields never leave your side.
AI approved by legal and complianceThird-party models used without third-party data exposureA defensible answer to what leaves your network
View serviceModels and infrastructure
The layer underneath. Usually part of a larger build, but people look for these on their own.
For behavior a prompt cannot hold. Trained against an eval set, with the base-model comparison that proves it earned its place.
Consistent house format across every outputA smaller model matching a larger one on your taskA documented reason the fine-tune exists
View serviceFor data that cannot leave the machine. Quantized models inside signed desktop builds, with the latency and memory work that makes them usable.
Products that work with no data leaving the deviceLatency with no network round tripDeployments in environments that forbid external calls
View serviceFor teams who will not call an API. Serving, batching and autoscaling tuned to your traffic shape, with throughput and cost measured rather than promised.
Open models in production on your own infrastructureMeasured throughput and cost, not projectedIndependence from a single provider
View serviceFor agents that need to reach your systems. Scoped tokens, per-tool permissions and an audit record on every call, through a layer you control.
Agents reaching email, CRM, cloud and internal systems safelyA tool layer reusable across every agent you buildCredentials that never reach the model
View serviceAround the AI
Everything else a build needs. Listed so nobody wonders whether it is in scope. It is, and it is the same engagement.
For teams postponing the risky changes. Plain-English requests turned into validated infrastructure, applied in your own account, with rollback written first.
A DevOps agent scoped to your own accountCloud setup and modernization without lock-inInfrastructure with safe apply and one-click rollback
View serviceFor teams who have the model but not the product. The interface around it: streaming, interruption and error states, and desktop builds that keep data local.
Customer-facing AI products on web and mobileNative desktop apps with on-device inferenceChat and copilot interfaces that feel instant
View serviceFor data that is not retrieval-ready. Connectors, normalization, chunking and indexing: the work that decides whether retrieval finds anything worth citing.
A corpus that retrieval can actually work againstFreshness without a full reindexSource lineage carried through to the answer
View serviceAnyone’s demo can call a model. The distance to production is the unglamorous middle.
Whichever of the six you buy, the delivery framework underneath is the same: a scoped token at identity, the plan-act-check-adjust loop on the agent runtime, personal data encoded through the PII vault before a model router picks for cost and latency, guardrails verdicting on the way out to your systems, and a record on the observability ledger at every step.
Each one is a working system, built in your repositories under your IP by the team that scoped it. They differ in how much of the delivery we run.
Choosing between the three is the engagement guide. Choosing between an implementation partner, building in-house, and an agency is the build-or-partner guide.
Your team built something that works in the demo and stalls on real inputs. We build the eval set, route models for cost and latency, lock down the data path, and put a safety net under it before it takes traffic.
Best when you have a prototype that will not cross into production.
You bring the problem and we deliver the working system. Discovery, build, evals, and deployment sit under one point of accountability, against a scope and price agreed before we start.
Best when the problem is clear and you want the result, not a team to manage.
A studio team of engineers and QA, led by our founder as your fractional CTO, owns delivery on your AI roadmap and makes the hard technical calls. You bring the product context.
Best when you have a roadmap and no senior AI team to run it.
Start from the failure it has to survive. Bigcircle maps your sentence onto where to start: a process run by hand across four different systems, documents nobody trusts without the source, a demo that falls over on real traffic, postponed cloud changes, customers waiting on hold, or a model with no product around it yet. If more than one is true, they are usually one engagement. If none fits, Bigcircle will say whether it is the right team.
They are the delivery framework under every Bigcircle service. Identity issues a scoped token. The agent runtime runs the plan-act-check-adjust loop. Personal data is encoded through the PII vault before a model router picks for cost and latency. Guardrails return a verdict before anything reaches your systems, and the observability ledger keeps a record at every step. Whichever of the six you buy, that framework underneath is the same.
Choose by what you already have. Production hardening fits when your team built a prototype that will not cross into production, and Bigcircle adds an eval set, model routing for cost and latency, a locked-down data path, and a safety net. A fixed-scope build fits when the problem is clear and you want the result. A studio-led programme fits when you have a roadmap and no senior AI team to run it.
You own the code in all three, and the work happens in your repositories under your IP and confidentiality terms. In a fixed-scope build Bigcircle owns the delivery: discovery, build, evals, and deployment against a fixed scope and price. Each service page names the mechanism, the case Bigcircle already ran, and what you own in your repository. This index is where you pick the failure that engagement has to survive.
Tell us what you're building. We'll tell you whether it needs a fixed-scope build, a prototype taken to production, or a programme we run, what's achievable, and where the real bottlenecks are.