Softcery builds conversational AI for B2B software platforms: agents that speak, type, and operate the product.

We work with software companies that are adding a conversational layer to their product or already run one. Some come to us before their first agent, some with an agent that is not ready for production, and some when they outgrow a rented platform. What we build ships as their technology, under their brand.

Services004

Advise

We stay available and answer questions with work.

  • Audits. A review of your existing layer: architecture, conversation quality, latency, cost, and security, with findings you can act on with any team.
  • Selection. Vendor, model, and build-or-buy decisions, made against measurements.
  • Proofs of concept. A working prototype that settles a feasibility question before you commit budget.
  • Adversarial testing. Deliberate attacks on your agent: prompt injection, jailbreaks, tool misuse, and behavior under failure, reported with reproductions.
  • Narrow builds. A specific, hard piece of engineering your team is not staffed for, delivered as a working component.

An availability retainer covers the access; each piece of work is its own small SOW.

Deploy

We bring conversational AI systems into production, with performance, reliability, and operating cost engineered for your volume.

  • Model serving. Speech recognition, speech synthesis, and language models running in production, self-hosted or API-based. Open models rarely perform out of the box; we tune them, and patch the upstream projects when needed.
  • Infrastructure. Hardware and cloud chosen for your volume and data requirements, sized against measured load.
  • Performance. The latency path from microphone to speaker, measured and budgeted per component against a response-time target.
  • Cost. Per-conversation economics measured and engineered: what each minute costs, and where the next saving is.

Priced as a SOW.

Build

We engineer the AI side of your product.

  • Copilots and agents. In-product assistants that operate your platform for the user, in text, voice, or both.
  • AI features. The functionality around the conversation: summaries, extraction, classification, and the rest of what your product needs from the models.
  • Integrations. Connections into your systems and external APIs, so the agent can act rather than only answer.
  • Interfaces. The surfaces your customers talk to: in-product chat and voice, web, and telephony.

Priced as a SOW.

Operate

We keep your agent running in production, for agents we built and agents built elsewhere.

  • Incidents. Production problems investigated and resolved.
  • Updates. Models and components kept current as the market moves, with changes tested before they ship.
  • Monitoring. Telemetry, alerts, and regular reporting on quality and cost.
  • Infrastructure. For self-hosted systems, the operations work underneath: capacity, upgrades, scaling, and recovery.
  • Improvement. Ongoing work on real conversations: quality raised, costs lowered, capabilities extended.

Priced as a retainer, with scope set in the agreement.

The stack
Runtime
ORCHESTRATION · TURN-TAKING
TEXT · VOICE · VISUAL SURFACES.C1
Speech
STT + TTS · OPEN OR MANAGED
SELF-HOSTED ON YOUR HARDWARE.C2
Models
OPEN-WEIGHT + API LLMS
TUNED ON YOUR TRANSCRIPTS.C3
Transport
WEBRTC + TELEPHONY
IN-PRODUCT · WEB · PHONE.C4
Connectors
MCP CLIENT + SERVER
YOUR SYSTEMS · EXTERNAL APIS.C5
Reliability
OPENTELEMETRY · LGTM
EVALS · LLM JUDGE · REPLAY.C6
SELF-HOSTABLESWAPPABLELICENSED SOURCE

Every team that builds a customer-facing conversational layer ends up assembling the same system: benchmarked components, an agent runtime, an eval harness, telemetry, operations. We have already built it. Our stack comes out of our production deployments and is maintained against them. Starting from it means skipping the work of deriving the same system yourself, along with the mistakes we have already made and fixed.

It covers the agent runtime, with orchestration, turn-taking, interruptions, and control of text, voice, and visual surfaces; speech recognition and synthesis; open-weight and API language models; WebRTC and telephony transport; MCP connectors into product systems and external APIs; and a reliability layer built on OpenTelemetry, with an eval harness that includes an LLM judge and transcript replay. Most components can be self-hosted, and every component can be swapped.

The stack fits at the start of a build, or at the exit from a rented platform. It is not for teams with a working system of their own; there is no reason to migrate off one. It comes under license: a full copy of the source, yours to run, modify, and extend, subject to a non-compete.

This website runs on the stack, and the agent on this page runs the website.